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Sample records for random-key genetic algorithm

  1. Biased Random Key Genetic Algorithm with Hybrid Decoding for Multi-objective Optimization

    OpenAIRE

    Tangpattanakul, Panwadee; Jozefowiez, Nicolas; Lopez, Pierre

    2013-01-01

    International audience; A biased random key genetic algorithm (BRKGA) is an efficient method for solving combinatorial optimization problems. It can be applied to solve both single-objective and multi-objective optimization problems. The BRKGA operates on a chromosome encoded as a key vector of real values between [0,1]. Generally, the chromosome has to be decoded by using a single decoding method in order to obtain a feasible solution. This paper presents a hybrid decoding, which combines th...

  2. Biased Random-Key Genetic Algorithms for the Winner Determination Problem in Combinatorial Auctions.

    Science.gov (United States)

    de Andrade, Carlos Eduardo; Toso, Rodrigo Franco; Resende, Mauricio G C; Miyazawa, Flávio Keidi

    2015-01-01

    In this paper we address the problem of picking a subset of bids in a general combinatorial auction so as to maximize the overall profit using the first-price model. This winner determination problem assumes that a single bidding round is held to determine both the winners and prices to be paid. We introduce six variants of biased random-key genetic algorithms for this problem. Three of them use a novel initialization technique that makes use of solutions of intermediate linear programming relaxations of an exact mixed integer linear programming model as initial chromosomes of the population. An experimental evaluation compares the effectiveness of the proposed algorithms with the standard mixed linear integer programming formulation, a specialized exact algorithm, and the best-performing heuristics proposed for this problem. The proposed algorithms are competitive and offer strong results, mainly for large-scale auctions.

  3. Penyelesaian CCVRPTW Menggunakan Biased Random Key Genetic Algorithm-Populasi Degradasi

    Directory of Open Access Journals (Sweden)

    Listy Avri Christiana

    2017-07-01

    Full Text Available The article presents the Biased Random Key Genetic Algorithm-Population Degradation (BRKGA-PD design for completing Capacitated Closed Vehicle Routing Problem with Time Windows (CCVRPTW on soft drink distributions that have been studied by Sembiring (2008. The goal is to determine some closed routes in meeting consumer demand with time limits and limit the capacity of vehicles used, so the total cost of distribution is minimal. The proposed algorithm adopts the extinction of population size. BRKGA-PD is coded using Matlab programming with the best parameter setting. The resulting solution is a subrute with a minimum of distribution fee. This algorithm is compared with two other methods, namely BRKGA general and heuristic methods. The results of this study can be concluded that the BRKGA-PD method is able to improve the general BRKGA because with a time difference that is not significant can provide cost savings of Rp. 6.857,- and BRKGA-PD is better than heuristic method because it can save more cost Rp. 87.000,-.

  4. A nuclear reload optimization approach using a real coded genetic algorithm with random keys

    Energy Technology Data Exchange (ETDEWEB)

    Lima, Alan M.M. de; Schirru, Roberto; Medeiros, Jose A.C.C., E-mail: alan@lmp.ufrj.b, E-mail: schirru@lmp.ufrj.b, E-mail: canedo@lmp.ufrj.b [Universidade Federal do Rio de Janeiro (UFRJ), RJ (Brazil). Coordenacao dos Programas de Pos-Graduacao de Engenharia. Programa de Engenharia Nuclear

    2009-07-01

    The fuel reload of a Pressurized Water Reactor is made whenever the burn up of the fuel assemblies in the nucleus of the reactor reaches a certain value such that it is not more possible to maintain a critical reactor producing energy at nominal power. The problem of fuel reload optimization consists on determining the positioning of the fuel assemblies within the nucleus of the reactor in an optimized way to minimize the cost benefit relationship of fuel assemblies cost per maximum burn up, and also satisfying symmetry and safety restrictions. The fuel reload optimization problem difficulty grows exponentially with the number of fuel assemblies in the nucleus of the reactor. During decades the fuel reload optimization problem was solved manually by experts that used their knowledge and experience to build configurations of the reactor nucleus, and testing them to verify if safety restrictions of the plant are satisfied. To reduce this burden, several optimization techniques have been used, included the binary code genetic algorithm. In this work we show the use of a real valued coded approach of the genetic algorithm, with different recombination methods, together with a transformation mechanism called random keys, to transform the real values of the genes of each chromosome in a combination of discrete fuel assemblies for evaluation of the reload optimization. Four different recombination methods were tested: discrete recombination, intermediate recombination, linear recombination and extended linear recombination. For each of the 4 recombination methods 10 different tests using different seeds for the random number generator were conducted 10 generating, totaling 40 tests. The results of the application of the genetic algorithm are shown with formulation of real numbers for the problem of the nuclear reload of the plant Angra 1 type PWR. Since the best results in the literature for this problem were found by the parallel PSO we will it use for comparison

  5. A Parallel Biased Random-Key Genetic Algorithm with Multiple Populations Applied to Irregular Strip Packing Problems

    Directory of Open Access Journals (Sweden)

    Bonfim Amaro Júnior

    2017-01-01

    Full Text Available The irregular strip packing problem (ISPP is a class of cutting and packing problem (C&P in which a set of items with arbitrary formats must be placed in a container with a variable length. The aim of this work is to minimize the area needed to accommodate the given demand. ISPP is present in various types of industries from manufacturers to exporters (e.g., shipbuilding, clothes, and glass. In this paper, we propose a parallel Biased Random-Key Genetic Algorithm (µ-BRKGA with multiple populations for the ISPP by applying a collision-free region (CFR concept as the positioning method, in order to obtain an efficient and fast layout solution. The layout problem for the proposed algorithm is represented by the placement order into the container and the corresponding orientation. In order to evaluate the proposed (µ-BRKGA algorithm, computational tests using benchmark problems were applied, analyzed, and compared with different approaches.

  6. Biased random key genetic algorithm with insertion and gender selection for capacitated vehicle routing problem with time windows

    Science.gov (United States)

    Rochman, Auliya Noor; Prasetyo, Hari; Nugroho, Munajat Tri

    2017-06-01

    Vehicle Routing Problem (VRP) often occurs when the manufacturers need to distribute their product to some customers/outlets. The distribution process is typically restricted by the capacity of the vehicle and the working hours at the distributor. This type of VRP is also known as Capacitated Vehicle Routing Problem with Time Windows (CVRPTW). A Biased Random Key Genetic Algorithm (BRKGA) was designed and coded in MATLAB to solve the CVRPTW case of soft drink distribution. The standard BRKGA was then modified by applying chromosome insertion into the initial population and defining chromosome gender for parent undergoing crossover operation. The performance of the established algorithms was then compared to a heuristic procedure for solving a soft drink distribution. Some findings are revealed (1) the total distribution cost of BRKGA with insertion (BRKGA-I) results in a cost saving of 39% compared to the total cost of heuristic method, (2) BRKGA with the gender selection (BRKGA-GS) could further improve the performance of the heuristic method. However, the BRKGA-GS tends to yield worse results compared to that obtained from the standard BRKGA.

  7. Genetic algorithms

    Science.gov (United States)

    Wang, Lui; Bayer, Steven E.

    1991-01-01

    Genetic algorithms are mathematical, highly parallel, adaptive search procedures (i.e., problem solving methods) based loosely on the processes of natural genetics and Darwinian survival of the fittest. Basic genetic algorithms concepts are introduced, genetic algorithm applications are introduced, and results are presented from a project to develop a software tool that will enable the widespread use of genetic algorithm technology.

  8. An Efficient Genetic Agorithm for Solving the Multi-Mode Resource-Constrained Project Scheduling Problem Based on Random Key Representation

    Directory of Open Access Journals (Sweden)

    Mohammad Hassan Sebt

    2015-11-01

    Full Text Available In this paper, a new genetic algorithm (GA is presented for solving the multi-mode resource-constrained project scheduling problem (MRCPSP with minimization of project makespan as the objective subject to resource and precedence constraints. A random key and the related mode list (ML representation scheme are used as encoding schemes and the multi-mode serial schedule generation scheme (MSSGS is considered as the decoding procedure. In this paper, a simple, efficient fitness function is proposed which has better performance compared to the other fitness functions in the literature. Defining a new mutation operator for ML is the other contribution of the current study. Comparing the results of the proposed GA with other approaches using the well-known benchmark sets in PSPLIB validates the effectiveness of the proposed algorithm to solve the MRCPSP.

  9. Software For Genetic Algorithms

    Science.gov (United States)

    Wang, Lui; Bayer, Steve E.

    1992-01-01

    SPLICER computer program is genetic-algorithm software tool used to solve search and optimization problems. Provides underlying framework and structure for building genetic-algorithm application program. Written in Think C.

  10. Fluid Genetic Algorithm (FGA)

    OpenAIRE

    Jafari-Marandi, Ruholla; Smith, Brian K.

    2017-01-01

    Genetic Algorithm (GA) has been one of the most popular methods for many challenging optimization problems when exact approaches are too computationally expensive. A review of the literature shows extensive research attempting to adapt and develop the standard GA. Nevertheless, the essence of GA which consists of concepts such as chromosomes, individuals, crossover, mutation, and others rarely has been the focus of recent researchers. In this paper method, Fluid Genetic Algorithm (FGA), some ...

  11. Genetic Algorithm Parameter Analysis

    OpenAIRE

    Ernesto, BELMONT-MORENO; Instituto de Fisica, UNAM

    2000-01-01

    The energy minimizing problem of atomic cluster configuration and the 2D spin glass problem are used for testing our genetic algorithm. It is shown to be crucial to adjust the degree of mutation and the population size for the efficiency of the algorithm.

  12. Genetic algorithm in chemistry.

    OpenAIRE

    da Costa, PA; Poppi, RJ

    1999-01-01

    Genetic algorithm is an optimization technique based on Darwin evolution theory. In last years its application in chemistry is increasing significantly due the special characteristics for optimization of complex systems. The basic principles and some further modifications implemented to improve its performance are presented, as well as a historical development. A numerical example of a function optimization is also shown to demonstrate how the algorithm works in an optimization process. Final...

  13. Adaptive Genetic Algorithm

    OpenAIRE

    Jakobović, Domagoj; Golub, Marin

    1999-01-01

    In this paper we introduce an adaptive, 'self-contained' genetic algorithm (GA) with steady-state selection. This variant of GA utilizes empirically based methods for calculating its control parameters. The adaptive algorithm estimates the percentage of the population to be replaced with new individuals (generation gap). It chooses the solutions for crossover and varies the number of mutations, ail regarding the current population state. The state of the population is evaluated by observing s...

  14. Genetic algorithm eclipse mapping

    OpenAIRE

    Halevin, A. V.

    2008-01-01

    In this paper we analyse capabilities of eclipse mapping technique, based on genetic algorithm optimization. To model of accretion disk we used the "fire-flies" conception. This model allows us to reconstruct the distribution of radiating medium in the disk using less number of free parameters than in other methods. Test models show that we can achieve good approximation without optimizing techniques.

  15. Fluid Genetic Algorithm (FGA

    Directory of Open Access Journals (Sweden)

    Ruholla Jafari-Marandi

    2017-04-01

    Full Text Available Genetic Algorithm (GA has been one of the most popular methods for many challenging optimization problems when exact approaches are too computationally expensive. A review of the literature shows extensive research attempting to adapt and develop the standard GA. Nevertheless, the essence of GA which consists of concepts such as chromosomes, individuals, crossover, mutation, and others rarely has been the focus of recent researchers. In this paper method, Fluid Genetic Algorithm (FGA, some of these concepts are changed, removed, and furthermore, new concepts are introduced. The performance of GA and FGA are compared through seven benchmark functions. FGA not only shows a better success rate and better convergence control, but it can be applied to a wider range of problems including multi-objective and multi-level problems. Also, the application of FGA for a real engineering problem, Quadric Assignment Problem (AQP, is shown and experienced.

  16. A new genetic algorithm

    OpenAIRE

    Cerf, Raphaël

    1996-01-01

    Here is a new genetic algorithm. It is built by randomly perturbing a two operator crossover-selection scheme. Three conditions of biological relevance are imposed on the crossover. A new selection mechanism is used, which has the decisive advantage of preserving the diversity of the individuals in the population. The attractors of the unperturbed process are particular equifitness subsets of populations endowed with a rich structure. The random vanishing perturbations are t...

  17. Parallel Genetic Algorithm System

    OpenAIRE

    Nagaraju Sangepu; Vikram, K.

    2010-01-01

    Genetic Algorithm (GA) is a popular technique to find the optimum of transformation, because of its simple implementation procedure. In image processing GAs are used as a parameter-search-for procedure, this processing requires very high performance of the computer. Recently, parallel processing used to reduce the time by distributing the appropriate amount of work to each computer in the clustering system. The processing time reduces with the number of dedicated computers. Parallel implement...

  18. Foundations of genetic algorithms 1991

    CERN Document Server

    1991-01-01

    Foundations of Genetic Algorithms 1991 (FOGA 1) discusses the theoretical foundations of genetic algorithms (GA) and classifier systems.This book compiles research papers on selection and convergence, coding and representation, problem hardness, deception, classifier system design, variation and recombination, parallelization, and population divergence. Other topics include the non-uniform Walsh-schema transform; spurious correlations and premature convergence in genetic algorithms; and variable default hierarchy separation in a classifier system. The grammar-based genetic algorithm; condition

  19. Genetic algorithm essentials

    CERN Document Server

    Kramer, Oliver

    2017-01-01

    This book introduces readers to genetic algorithms (GAs) with an emphasis on making the concepts, algorithms, and applications discussed as easy to understand as possible. Further, it avoids a great deal of formalisms and thus opens the subject to a broader audience in comparison to manuscripts overloaded by notations and equations. The book is divided into three parts, the first of which provides an introduction to GAs, starting with basic concepts like evolutionary operators and continuing with an overview of strategies for tuning and controlling parameters. In turn, the second part focuses on solution space variants like multimodal, constrained, and multi-objective solution spaces. Lastly, the third part briefly introduces theoretical tools for GAs, the intersections and hybridizations with machine learning, and highlights selected promising applications.

  20. From Genetics to Genetic Algorithms

    Indian Academy of Sciences (India)

    The strings of artificial genetic systems are analogous to chromosomes. GENERAL I ARTICLE. GAs operate by combining the information present in different possible solutions for a given problem, in such a way that a better solution is obtained in future generations. Terminologies used in natural genetic systems (NGS) and.

  1. The asymptotic probabilistic genetic algorithm

    OpenAIRE

    Galushin, P.; Semenkin, E.

    2009-01-01

    This paper proposes the modification of probabilistic genetic algorithm, which uses genetic operators, not affecting the particular solutions, but the probabilities distribution of solution vector's components. This paper also compares the reliability and efficiency of the base algorithm and proposed modification using the set of test optimization problems and bank loan portfolio problem.

  2. Adaptive Beamforming Using Genetic Algorithms

    Directory of Open Access Journals (Sweden)

    Z. Raida

    1998-09-01

    Full Text Available The presented submission describes how genetic algorithms can be applied to the control of adaptive antennas. The proposed optimization method is easily implementable on one hand, but relatively slowly converging and depending on the parameters of the genetic algorithms on the other hand. The disadvantages as well as some possible improvements are discussed in this paper.

  3. A genetic engineering approach to genetic algorithms.

    Science.gov (United States)

    Gero, J S; Kazakov, V

    2001-01-01

    We present an extension to the standard genetic algorithm (GA), which is based on concepts of genetic engineering. The motivation is to discover useful and harmful genetic materials and then execute an evolutionary process in such a way that the population becomes increasingly composed of useful genetic material and increasingly free of the harmful genetic material. Compared to the standard GA, it provides some computational advantages as well as a tool for automatic generation of hierarchical genetic representations specifically tailored to suit certain classes of problems.

  4. The Distributed Genetic Algorithm Revisited

    OpenAIRE

    Belding, Theodore C.

    1995-01-01

    This paper extends previous work done by Tanese on the distributed genetic algorithm (DGA). Tanese found that the DGA outperformed the canonical serial genetic algorithm (CGA) on a class of difficult, randomly-generated Walsh polynomials. This left open the question of whether the DGA would have similar success on functions that were more amenable to optimization by the CGA. In this work, experiments were done to compare the DGA's performance on the Royal Road class of fitness functions to th...

  5. Genetic K-Means Algorithm

    OpenAIRE

    Krishna, K; Murty, Narasimha M

    1999-01-01

    In this paper, we propose a novel hybrid genetic algorithm (GA) that finds a globally optimal partition of a given data into a specified number of clusters. GAs used earlier in clustering employ either an expensive crossover operator to generate valid child chromosomes from parent chromosomes or a costly fitness function or both. To circumvent these expensive operations, we hybridize GA with a classical gradient descent algorithm used in clustering viz., K-means algorithm. Hence, the name gen...

  6. Deceptiveness and genetic algorithm dynamics

    Energy Technology Data Exchange (ETDEWEB)

    Liepins, G.E. (Oak Ridge National Lab., TN (USA)); Vose, M.D. (Tennessee Univ., Knoxville, TN (USA))

    1990-01-01

    We address deceptiveness, one of at least four reasons genetic algorithms can fail to converge to function optima. We construct fully deceptive functions and other functions of intermediate deceptiveness. For the fully deceptive functions of our construction, we generate linear transformations that induce changes of representation to render the functions fully easy. We further model genetic algorithm selection recombination as the interleaving of linear and quadratic operators. Spectral analysis of the underlying matrices allows us to draw preliminary conclusions about fixed points and their stability. We also obtain an explicit formula relating the nonuniform Walsh transform to the dynamics of genetic search. 21 refs.

  7. An Improved Genetic Algorithm for Developing Deterministic OTP Key Generator

    Directory of Open Access Journals (Sweden)

    Ashish Jain

    2017-01-01

    Full Text Available Recently, a genetic-based random key generator (GRKG for the one-time pad (OTP cryptosystem has been proposed in the literature which has certain limitations. In this paper, two main characteristics (speed and randomness of the GRKG method are significantly improved by presenting the IGRKG method (improved genetic-based random key generator method. The proposed IGRKG method generates an initial pad by using linear congruential generator (LCG and improves the randomness of the initial pad using genetic algorithm. There are three reasons behind the use of LCG: it is easy to implement, it can run efficiently on computer hardware, and it has good statistical properties. The experimental results show the superiority of the IGRKG over GRKG in terms of speed and randomness. Hereby we would like to mention that no prior experimental work has been presented in the literature which is directly related to the OTP key generation using evolutionary algorithms. Therefore, this work can be considered as a guideline for future research.

  8. A Process Algebra Genetic Algorithm

    OpenAIRE

    Karaman, Sertac; Shima, Tal; Frazzoli, Emilio

    2011-01-01

    A genetic algorithm that utilizes process algebra for coding of solution chromosomes and for defining evolutionary based operators is presented. The algorithm is applicable to mission planning and optimization problems. As an example the high level mission planning for a cooperative group of uninhabited aerial vehicles is investigated. The mission planning problem is cast as an assignment problem, and solutions to the assignment problem are given in the form of chromosomes that are manipulate...

  9. Genetic Algorithm based Gait Recognition

    OpenAIRE

    R.Ashok Kumar Reddy; G. Venkata Narasimhulu; Dr. S. A. K. Jilani; Dr D.Seshappa

    2013-01-01

    In this paper, a face/gait recognition system for personal identification and verification using genetic algorithm. This face/gait Recognition System (FRS/GRS) is also being trained for gender identification. The Face/Gait recognition system consists of three steps. At the very outset some pre-processing are applied on the input image. Secondly face/gait features are extracted, which will be taken as the input of the BPNN and genetic algorithm (GA) in the third step and classification is carr...

  10. Genetic algorithm optimization of entanglement

    OpenAIRE

    Navarro-Munoz, Jorge C.; Rosu, H. C.; Lopez-Sandoval, R.

    2006-01-01

    We present an application of a genetic algorithmic computational method to the optimization of the concurrence measure of entanglement for the cases of one dimensional chains, as well as square and triangular lattices in a simple tight-binding approach in which the hopping of electrons is much stronger than the phonon dissipation

  11. Simultaneous stabilization using genetic algorithms

    Energy Technology Data Exchange (ETDEWEB)

    Benson, R.W.; Schmitendorf, W.E. (California Univ., Irvine, CA (USA). Dept. of Mechanical Engineering)

    1991-01-01

    This paper considers the problem of simultaneously stabilizing a set of plants using full state feedback. The problem is converted to a simple optimization problem which is solved by a genetic algorithm. Several examples demonstrate the utility of this method. 14 refs., 8 figs.

  12. Genetic Algorithm for Optimization: Preprocessor and Algorithm

    Science.gov (United States)

    Sen, S. K.; Shaykhian, Gholam A.

    2006-01-01

    Genetic algorithm (GA) inspired by Darwin's theory of evolution and employed to solve optimization problems - unconstrained or constrained - uses an evolutionary process. A GA has several parameters such the population size, search space, crossover and mutation probabilities, and fitness criterion. These parameters are not universally known/determined a priori for all problems. Depending on the problem at hand, these parameters need to be decided such that the resulting GA performs the best. We present here a preprocessor that achieves just that, i.e., it determines, for a specified problem, the foregoing parameters so that the consequent GA is a best for the problem. We stress also the need for such a preprocessor both for quality (error) and for cost (complexity) to produce the solution. The preprocessor includes, as its first step, making use of all the information such as that of nature/character of the function/system, search space, physical/laboratory experimentation (if already done/available), and the physical environment. It also includes the information that can be generated through any means - deterministic/nondeterministic/graphics. Instead of attempting a solution of the problem straightway through a GA without having/using the information/knowledge of the character of the system, we would do consciously a much better job of producing a solution by using the information generated/created in the very first step of the preprocessor. We, therefore, unstintingly advocate the use of a preprocessor to solve a real-world optimization problem including NP-complete ones before using the statistically most appropriate GA. We also include such a GA for unconstrained function optimization problems.

  13. Parameter Selection in Genetic Algorithms

    Directory of Open Access Journals (Sweden)

    Onur BOYABATLI

    2004-08-01

    Full Text Available In this study, we provide a new taxonomy of parameters of genetic algorithms (GA, structural and numerical parameters, and analyze the effect of numerical parameters on the performance of GA based simulation optimization applications with experimental design techniques. Appropriate levels of each parameter are proposed for a particular problem domain. Controversial to existing literature on GA, our computational results reveal that in the case of a dominant set of decision variable the crossover operator does not have a significant impact on the performance measures, whereas high mutation rates are more suitable for GA applications.

  14. Learning Intelligent Genetic Algorithms Using Japanese Nonograms

    Science.gov (United States)

    Tsai, Jinn-Tsong; Chou, Ping-Yi; Fang, Jia-Cen

    2012-01-01

    An intelligent genetic algorithm (IGA) is proposed to solve Japanese nonograms and is used as a method in a university course to learn evolutionary algorithms. The IGA combines the global exploration capabilities of a canonical genetic algorithm (CGA) with effective condensed encoding, improved fitness function, and modified crossover and…

  15. New Results in Astrodynamics Using Genetic Algorithms

    Science.gov (United States)

    Coverstone-Carroll, V.; Hartmann, J. W.; Williams, S. N.; Mason, W. J.

    1998-01-01

    Generic algorithms have gained popularity as an effective procedure for obtaining solutions to traditionally difficult space mission optimization problems. In this paper, a brief survey of the use of genetic algorithms to solve astrodynamics problems is presented and is followed by new results obtained from applying a Pareto genetic algorithm to the optimization of low-thrust interplanetary spacecraft missions.

  16. Genetic algorithms and fuzzy multiobjective optimization

    CERN Document Server

    Sakawa, Masatoshi

    2002-01-01

    Since the introduction of genetic algorithms in the 1970s, an enormous number of articles together with several significant monographs and books have been published on this methodology. As a result, genetic algorithms have made a major contribution to optimization, adaptation, and learning in a wide variety of unexpected fields. Over the years, many excellent books in genetic algorithm optimization have been published; however, they focus mainly on single-objective discrete or other hard optimization problems under certainty. There appears to be no book that is designed to present genetic algorithms for solving not only single-objective but also fuzzy and multiobjective optimization problems in a unified way. Genetic Algorithms And Fuzzy Multiobjective Optimization introduces the latest advances in the field of genetic algorithm optimization for 0-1 programming, integer programming, nonconvex programming, and job-shop scheduling problems under multiobjectiveness and fuzziness. In addition, the book treats a w...

  17. Nurse Scheduling Using Genetic Algorithm

    Directory of Open Access Journals (Sweden)

    Komgrit Leksakul

    2014-01-01

    Full Text Available This study applied engineering techniques to develop a nurse scheduling model that, while maintaining the highest level of service, simultaneously minimized hospital-staffing costs and equitably distributed overtime pay. In the mathematical model, the objective function was the sum of the overtime payment to all nurses and the standard deviation of the total overtime payment that each nurse received. Input data distributions were analyzed in order to formulate a simulation model to determine the optimal demand for nurses that met the hospital’s service standards. To obtain the optimal nurse schedule with the number of nurses acquired from the simulation model, we proposed a genetic algorithm (GA with two-point crossover and random mutation. After running the algorithm, we compared the expenses and number of nurses between the existing and our proposed nurse schedules. For January 2013, the nurse schedule obtained by GA could save 12% in staffing expenses per month and 13% in number of nurses when compare with the existing schedule, while more equitably distributing overtime pay between all nurses.

  18. A genetic algorithm approach for the single machine scheduling problem with linear earliness and quadratic tardiness penalties

    OpenAIRE

    Jorge M. S. Valente; José Fernando Gonçalves

    2008-01-01

    In this paper, we consider the single machine scheduling problem with linear earliness and quadratic tardiness costs, and no machine idle time. We propose a genetic approach based on a random key alphabet. Several genetic algorithms based on this approach are presented. These versions differ on the generation of the initial population, as well as on the use of local search. The proposed procedures are compared with the best existing heuristic, as well as with optimal solutions for the smaller...

  19. Dynamic traffic assignment : genetic algorithms approach

    Science.gov (United States)

    1997-01-01

    Real-time route guidance is a promising approach to alleviating congestion on the nations highways. A dynamic traffic assignment model is central to the development of guidance strategies. The artificial intelligence technique of genetic algorithm...

  20. Genetic algorithm for nuclear data evaluation

    Energy Technology Data Exchange (ETDEWEB)

    Arthur, Jennifer Ann [Los Alamos National Lab. (LANL), Los Alamos, NM (United States)

    2018-02-02

    These are slides on genetic algorithm for nuclear data evaluation. The following is covered: initial population, fitness (outer loop), calculate fitness, selection (first part of inner loop), reproduction (second part of inner loop), solution, and examples.

  1. Using Genetic Algorithms for Real Estate Appraisals

    National Research Council Canada - National Science Library

    Vincenzo Del Giudice; Pierfrancesco De Paola; Fabiana Forte

    2017-01-01

    .... Also, to verify the reliability of genetic algorithms for real estate appraisals and, at the same time, to show the forecasting potentialities of these techniques in the analysis of housing markets...

  2. Genetic algorithms at UC Davis/LLNL

    Energy Technology Data Exchange (ETDEWEB)

    Vemuri, V.R. [comp.

    1993-12-31

    A tutorial introduction to genetic algorithms is given. This brief tutorial should serve the purpose of introducing the subject to the novice. The tutorial is followed by a brief commentary on the term project reports that follow.

  3. GARD: a genetic algorithm for recombination detection

    National Research Council Canada - National Science Library

    Kosakovsky Pond, Sergei L; Posada, David; Gravenor, Michael B; Woelk, Christopher H; Frost, Simon D W

    2006-01-01

    .... We developed a likelihood-based model selection procedure that uses a genetic algorithm to search multiple sequence alignments for evidence of recombination breakpoints and identify putative recombinant sequences...

  4. Genetic Algorithm Optimized Neural Networks Ensemble as ...

    African Journals Online (AJOL)

    NJD

    Genetic Algorithm Optimized Neural Networks Ensemble as. Calibration Model for Simultaneous Spectrophotometric. Estimation of Atenolol and Losartan Potassium in Tablets. Dondeti Satyanarayana*, Kamarajan Kannan and Rajappan Manavalan. Department of Pharmacy, Annamalai University, Annamalainagar, Tamil ...

  5. Genetic algorithms as global random search methods

    Science.gov (United States)

    Peck, Charles C.; Dhawan, Atam P.

    1995-01-01

    Genetic algorithm behavior is described in terms of the construction and evolution of the sampling distributions over the space of candidate solutions. This novel perspective is motivated by analysis indicating that the schema theory is inadequate for completely and properly explaining genetic algorithm behavior. Based on the proposed theory, it is argued that the similarities of candidate solutions should be exploited directly, rather than encoding candidate solutions and then exploiting their similarities. Proportional selection is characterized as a global search operator, and recombination is characterized as the search process that exploits similarities. Sequential algorithms and many deletion methods are also analyzed. It is shown that by properly constraining the search breadth of recombination operators, convergence of genetic algorithms to a global optimum can be ensured.

  6. An investigation of messy genetic algorithms

    Science.gov (United States)

    Goldberg, David E.; Deb, Kalyanmoy; Korb, Bradley

    1990-01-01

    Genetic algorithms (GAs) are search procedures based on the mechanics of natural selection and natural genetics. They combine the use of string codings or artificial chromosomes and populations with the selective and juxtapositional power of reproduction and recombination to motivate a surprisingly powerful search heuristic in many problems. Despite their empirical success, there has been a long standing objection to the use of GAs in arbitrarily difficult problems. A new approach was launched. Results to a 30-bit, order-three-deception problem were obtained using a new type of genetic algorithm called a messy genetic algorithm (mGAs). Messy genetic algorithms combine the use of variable-length strings, a two-phase selection scheme, and messy genetic operators to effect a solution to the fixed-coding problem of standard simple GAs. The results of the study of mGAs in problems with nonuniform subfunction scale and size are presented. The mGA approach is summarized, both its operation and the theory of its use. Experiments on problems of varying scale, varying building-block size, and combined varying scale and size are presented.

  7. Genetic Algorithms to Optimizatize Lecturer Assessment's Criteria

    Science.gov (United States)

    Jollyta, Deny; Johan; Hajjah, Alyauma

    2017-12-01

    The lecturer assessment criteria is used as a measurement of the lecturer's performance in a college environment. To determine the value for a criteriais complicated and often leads to doubt. The absence of a standard valuefor each assessment criteria will affect the final results of the assessment and become less presentational data for the leader of college in taking various policies relate to reward and punishment. The Genetic Algorithm comes as an algorithm capable of solving non-linear problems. Using chromosomes in the random initial population, one of the presentations is binary, evaluates the fitness function and uses crossover genetic operator and mutation to obtain the desired crossbreed. It aims to obtain the most optimum criteria values in terms of the fitness function of each chromosome. The training results show that Genetic Algorithm able to produce the optimal values of lecturer assessment criteria so that can be usedby the college as a standard value for lecturer assessment criteria.

  8. Genetic algorithm solution for double digest problem

    Science.gov (United States)

    Ganjtabesh, Mohammad; Ahrabian, H; Nowzari-Dalini, A; Kashani Moghadam, Z Razaghi

    2012-01-01

    The strongly NP-Hard Double Digest Problem, for reconstructing the physical map of DNA sequence, in now using for efficient genotyping. Most of the existing methods are inefficient in tackling large instances due to the large search space for the problem which grows as a factorial function (a!)(b!) of the numbers a and b of the DNA fragments generated by the two restriction enzymes. Also, none of the existing methods are able to handle the erroneous data. In this paper, we develop a novel method based on genetic algorithm for solving this problem and it is adapted to handle the erroneous data. Our genetic algorithm is implemented and compared with the other well-known existing algorithms. The obtained results show the efficiency (speedup) of our algorithm with respect to the other methods, specially for erroneous data. PMID:22715298

  9. Genetic warfarin dosing: tables versus algorithms.

    Science.gov (United States)

    Finkelman, Brian S; Gage, Brian F; Johnson, Julie A; Brensinger, Colleen M; Kimmel, Stephen E

    2011-02-01

    The aim of this study was to compare the accuracy of genetic tables and formal pharmacogenetic algorithms for warfarin dosing. Pharmacogenetic algorithms based on regression equations can predict warfarin dose, but they require detailed mathematical calculations. A simpler alternative, recently added to the warfarin label by the U.S. Food and Drug Administration, is to use genotype-stratified tables to estimate warfarin dose. This table may potentially increase the use of pharmacogenetic warfarin dosing in clinical practice; however, its accuracy has not been quantified. A retrospective cohort study of 1,378 patients from 3 anticoagulation centers was conducted. Inclusion criteria were stable therapeutic warfarin dose and complete genetic and clinical data. Five dose prediction methods were compared: 2 methods using only clinical information (empiric 5 mg/day dosing and a formal clinical algorithm), 2 genetic tables (the new warfarin label table and a table based on mean dose stratified by genotype), and 1 formal pharmacogenetic algorithm, using both clinical and genetic information. For each method, the proportion of patients whose predicted doses were within 20% of their actual therapeutic doses was determined. Dosing methods were compared using McNemar's chi-square test. Warfarin dose prediction was significantly more accurate (all p algorithm (52%) than with all other methods: empiric dosing (37%; odds ratio [OR]: 2.2), clinical algorithm (39%; OR: 2.2), warfarin label (43%; OR: 1.8), and genotype mean dose table (44%; OR: 1.9). Although genetic tables predicted warfarin dose better than empiric dosing, formal pharmacogenetic algorithms were the most accurate. Copyright © 2011 American College of Cardiology Foundation. Published by Elsevier Inc. All rights reserved.

  10. Evolving Temporal Association Rules with Genetic Algorithms

    Science.gov (United States)

    Matthews, Stephen G.; Gongora, Mario A.; Hopgood, Adrian A.

    A novel framework for mining temporal association rules by discovering itemsets with a genetic algorithm is introduced. Metaheuristics have been applied to association rule mining, we show the efficacy of extending this to another variant - temporal association rule mining. Our framework is an enhancement to existing temporal association rule mining methods as it employs a genetic algorithm to simultaneously search the rule space and temporal space. A methodology for validating the ability of the proposed framework isolates target temporal itemsets in synthetic datasets. The Iterative Rule Learning method successfully discovers these targets in datasets with varying levels of difficulty.

  11. Genetic algorithm for neural networks optimization

    Science.gov (United States)

    Setyawati, Bina R.; Creese, Robert C.; Sahirman, Sidharta

    2004-11-01

    This paper examines the forecasting performance of multi-layer feed forward neural networks in modeling a particular foreign exchange rates, i.e. Japanese Yen/US Dollar. The effects of two learning methods, Back Propagation and Genetic Algorithm, in which the neural network topology and other parameters fixed, were investigated. The early results indicate that the application of this hybrid system seems to be well suited for the forecasting of foreign exchange rates. The Neural Networks and Genetic Algorithm were programmed using MATLAB«.

  12. Optimized Design of Microresonators Using Genetic Algorithm

    Directory of Open Access Journals (Sweden)

    G.Uma

    2006-10-01

    Full Text Available This paper represents the optimization of micro resonator design using Genetic Algorithm. Optimized physical layout parameters are generated using genetic algorithm. Optimization evaluates parameter by minimizing active device area, electrostatic drive voltage or a weighted combination of area and drive voltage or by maximizing displacement at resonance. Desired resonant frequency and mode frequency separations are governed by the objective function. Layouts are generated for optimized design parameters using Coventorware. Modal analysis is performed and it is compared with the designed resonant frequency.

  13. The Applications of Genetic Algorithms in Medicine

    Science.gov (United States)

    Ghaheri, Ali; Shoar, Saeed; Naderan, Mohammad; Hoseini, Sayed Shahabuddin

    2015-01-01

    A great wealth of information is hidden amid medical research data that in some cases cannot be easily analyzed, if at all, using classical statistical methods. Inspired by nature, metaheuristic algorithms have been developed to offer optimal or near-optimal solutions to complex data analysis and decision-making tasks in a reasonable time. Due to their powerful features, metaheuristic algorithms have frequently been used in other fields of sciences. In medicine, however, the use of these algorithms are not known by physicians who may well benefit by applying them to solve complex medical problems. Therefore, in this paper, we introduce the genetic algorithm and its applications in medicine. The use of the genetic algorithm has promising implications in various medical specialties including radiology, radiotherapy, oncology, pediatrics, cardiology, endocrinology, surgery, obstetrics and gynecology, pulmonology, infectious diseases, orthopedics, rehabilitation medicine, neurology, pharmacotherapy, and health care management. This review introduces the applications of the genetic algorithm in disease screening, diagnosis, treatment planning, pharmacovigilance, prognosis, and health care management, and enables physicians to envision possible applications of this metaheuristic method in their medical career.] PMID:26676060

  14. The Applications of Genetic Algorithms in Medicine.

    Science.gov (United States)

    Ghaheri, Ali; Shoar, Saeed; Naderan, Mohammad; Hoseini, Sayed Shahabuddin

    2015-11-01

    A great wealth of information is hidden amid medical research data that in some cases cannot be easily analyzed, if at all, using classical statistical methods. Inspired by nature, metaheuristic algorithms have been developed to offer optimal or near-optimal solutions to complex data analysis and decision-making tasks in a reasonable time. Due to their powerful features, metaheuristic algorithms have frequently been used in other fields of sciences. In medicine, however, the use of these algorithms are not known by physicians who may well benefit by applying them to solve complex medical problems. Therefore, in this paper, we introduce the genetic algorithm and its applications in medicine. The use of the genetic algorithm has promising implications in various medical specialties including radiology, radiotherapy, oncology, pediatrics, cardiology, endocrinology, surgery, obstetrics and gynecology, pulmonology, infectious diseases, orthopedics, rehabilitation medicine, neurology, pharmacotherapy, and health care management. This review introduces the applications of the genetic algorithm in disease screening, diagnosis, treatment planning, pharmacovigilance, prognosis, and health care management, and enables physicians to envision possible applications of this metaheuristic method in their medical career.].

  15. Predicting complex mineral structures using genetic algorithms.

    Science.gov (United States)

    Mohn, Chris E; Kob, Walter

    2015-10-28

    We show that symmetry-adapted genetic algorithms are capable of finding the ground state of a range of complex crystalline phases including layered- and incommensurate super-structures. This opens the way for the atomistic prediction of complex crystal structures of functional materials and mineral phases.

  16. Learning to forecast: Genetic algorithms and experiments

    NARCIS (Netherlands)

    Makarewicz, T.A.

    2014-01-01

    The central question that this thesis addresses is how economic agents learn to form price expectations, which are a crucial element of macroeconomic and financial models. The thesis applies a Genetic Algorithms model of learning to previous laboratory experiments, explaining the observed

  17. Genetic Algorithms for Multiple-Choice Problems

    Science.gov (United States)

    Aickelin, Uwe

    2010-04-01

    This thesis investigates the use of problem-specific knowledge to enhance a genetic algorithm approach to multiple-choice optimisation problems.It shows that such information can significantly enhance performance, but that the choice of information and the way it is included are important factors for success.Two multiple-choice problems are considered.The first is constructing a feasible nurse roster that considers as many requests as possible.In the second problem, shops are allocated to locations in a mall subject to constraints and maximising the overall income.Genetic algorithms are chosen for their well-known robustness and ability to solve large and complex discrete optimisation problems.However, a survey of the literature reveals room for further research into generic ways to include constraints into a genetic algorithm framework.Hence, the main theme of this work is to balance feasibility and cost of solutions.In particular, co-operative co-evolution with hierarchical sub-populations, problem structure exploiting repair schemes and indirect genetic algorithms with self-adjusting decoder functions are identified as promising approaches.The research starts by applying standard genetic algorithms to the problems and explaining the failure of such approaches due to epistasis.To overcome this, problem-specific information is added in a variety of ways, some of which are designed to increase the number of feasible solutions found whilst others are intended to improve the quality of such solutions.As well as a theoretical discussion as to the underlying reasons for using each operator,extensive computational experiments are carried out on a variety of data.These show that the indirect approach relies less on problem structure and hence is easier to implement and superior in solution quality.

  18. Quantum Genetic Algorithms for Computer Scientists

    Directory of Open Access Journals (Sweden)

    Rafael Lahoz-Beltra

    2016-10-01

    Full Text Available Genetic algorithms (GAs are a class of evolutionary algorithms inspired by Darwinian natural selection. They are popular heuristic optimisation methods based on simulated genetic mechanisms, i.e., mutation, crossover, etc. and population dynamical processes such as reproduction, selection, etc. Over the last decade, the possibility to emulate a quantum computer (a computer using quantum-mechanical phenomena to perform operations on data has led to a new class of GAs known as “Quantum Genetic Algorithms” (QGAs. In this review, we present a discussion, future potential, pros and cons of this new class of GAs. The review will be oriented towards computer scientists interested in QGAs “avoiding” the possible difficulties of quantum-mechanical phenomena.

  19. Predicting mining activity with parallel genetic algorithms

    Science.gov (United States)

    Talaie, S.; Leigh, R.; Louis, S.J.; Raines, G.L.; Beyer, H.G.; O'Reilly, U.M.; Banzhaf, Arnold D.; Blum, W.; Bonabeau, C.; Cantu-Paz, E.W.; ,; ,

    2005-01-01

    We explore several different techniques in our quest to improve the overall model performance of a genetic algorithm calibrated probabilistic cellular automata. We use the Kappa statistic to measure correlation between ground truth data and data predicted by the model. Within the genetic algorithm, we introduce a new evaluation function sensitive to spatial correctness and we explore the idea of evolving different rule parameters for different subregions of the land. We reduce the time required to run a simulation from 6 hours to 10 minutes by parallelizing the code and employing a 10-node cluster. Our empirical results suggest that using the spatially sensitive evaluation function does indeed improve the performance of the model and our preliminary results also show that evolving different rule parameters for different regions tends to improve overall model performance. Copyright 2005 ACM.

  20. Application of Genetic Algorithms in Seismic Tomography

    Science.gov (United States)

    Soupios, Pantelis; Akca, Irfan; Mpogiatzis, Petros; Basokur, Ahmet; Papazachos, Constantinos

    2010-05-01

    In the earth sciences several inverse problems that require data fitting and parameter estimation are nonlinear and can involve a large number of unknown parameters. Consequently, the application of analytical inversion or optimization techniques may be quite restrictive. In practice, most analytical methods are local in nature and rely on a linearized form of the problem in question, adopting an iterative procedure using partial derivatives to improve an initial model. This approach can lead to a dependence of the final model solution on the starting model and is prone to entrapment in local misfit minima. Moreover, the calculation of derivatives can be computationally inefficient and create instabilities when numerical approximations are used. In contrast to these local minimization methods, global techniques that do not rely on partial derivatives, are independent of the form of the data misfit criterion, and are computationally robust. Such methods often use random processes to sample a selected wider span of the model space. In this situation, randomly generated models are assessed in terms of their data-fitting quality and the process may be stopped after a certain number of acceptable models is identified or continued until a satisfactory data fit is achieved. A new class of methods known as genetic algorithms achieves the aforementioned approximation through novel model representation and manipulations. Genetic algorithms (GAs) were originally developed in the field of artificial intelligence by John Holland more than 20 years ago, but even in this field it is less than a decade that the methodology has been more generally applied and only recently did the methodology attract the attention of the earth sciences community. Applications have been generally concentrated in geophysics and in particular seismology. As awareness of genetic algorithms grows there surely will be many more and varied applications to earth science problems. In the present work, the

  1. Genetic algorithms in adaptive fuzzy control

    Science.gov (United States)

    Karr, C. Lucas; Harper, Tony R.

    1992-01-01

    Researchers at the U.S. Bureau of Mines have developed adaptive process control systems in which genetic algorithms (GA's) are used to augment fuzzy logic controllers (FLC's). GA's are search algorithms that rapidly locate near-optimum solutions to a wide spectrum of problems by modeling the search procedures of natural genetics. FLC's are rule based systems that efficiently manipulate a problem environment by modeling the 'rule-of-thumb' strategy used in human decision making. Together, GA's and FLC's possess the capabilities necessary to produce powerful, efficient, and robust adaptive control systems. To perform efficiently, such control systems require a control element to manipulate the problem environment, an analysis element to recognize changes in the problem environment, and a learning element to adjust fuzzy membership functions in response to the changes in the problem environment. Details of an overall adaptive control system are discussed. A specific computer-simulated chemical system is used to demonstrate the ideas presented.

  2. Learning interactive learning strategy with genetic algorithm

    OpenAIRE

    Hanzel, Jan

    2014-01-01

    The main goal of this thesis was to develop an algoritem for learning the best strategy in the case of interactive learning between a human and a robot. We presented the definition and formalization of a learning strategy. A learning strategy specifies the behaviour of a student and a teacher in a interactive learning process. We also presented a genetic algorithm to resolve our optimisation problem. We tryed to inpruve vectors which are used to present learning strategies. The vectors were...

  3. Pavement maintenance scheduling using genetic algorithms

    OpenAIRE

    Yang, Chao; Remenyte-Prescott, Rasa; Andrews, John D.

    2015-01-01

    This paper presents a new pavement management system (PMS) to achieve the optimal pavement maintenance and rehabilitation (M&R) strategy for a highway network using genetic algorithms (GAs). Optimal M&R strategy is a set of pavement activities that both minimise the maintenance cost of a highway network and maximise the pavement condition of the road sections on the network during a certain planning period. NSGA-II, a multi-objective GA, is employed to perform pavement maintenance optimisatio...

  4. Convalesce Optimization for Input Allocation Problem Using Hybrid Genetic Algorithm

    OpenAIRE

    Mamta Madan; Sushila Madan

    2010-01-01

    Problem statement: The purpose of this study was to describe categories of hybrid genetic algorithm and validate that the hybrid genetic algorithm converges to the optimal solution rather than a near optimal solution so that Hybrid Genetic algorithms can be used to solve real world problems and receive significant interest. Approach: We implemented the input allocation problem for a manufacturing unit firstly with pure genetic algorithm using Matlab's GA tool and then compared the results wit...

  5. Research on PID control system based on genetic algorithm

    Science.gov (United States)

    Zhang, Dingqun; Yang, Xinfeng

    2011-12-01

    Genetic algorithms, there are many difficult issues, such as premature convergence, choice of control parameters. This combination of all improvements, the optimal preservation strategy, adaptive set the crossover probability and mutation probability, the idea of fitness scaling into the simple genetic algorithm, the algorithm is improved and used Matlab program to achieve the improved algorithm, prove the correctness and practicability of this method.

  6. Saving Resources with Plagues in Genetic Algorithms

    Energy Technology Data Exchange (ETDEWEB)

    de Vega, F F; Cantu-Paz, E; Lopez, J I; Manzano, T

    2004-06-15

    The population size of genetic algorithms (GAs) affects the quality of the solutions and the time required to find them. While progress has been made in estimating the population sizes required to reach a desired solution quality for certain problems, in practice the sizing of populations is still usually performed by trial and error. These trials might lead to find a population that is large enough to reach a satisfactory solution, but there may still be opportunities to optimize the computational cost by reducing the size of the population. This paper presents a technique called plague that periodically removes a number of individuals from the population as the GA executes. Recently, the usefulness of the plague has been demonstrated for genetic programming. The objective of this paper is to extend the study of plagues to genetic algorithms. We experiment with deceptive trap functions, a tunable difficult problem for GAs, and the experiments show that plagues can save computational time while maintaining solution quality and reliability.

  7. Optical flow optimization using parallel genetic algorithm

    Science.gov (United States)

    Zavala-Romero, Olmo; Botella, Guillermo; Meyer-Bäse, Anke; Meyer Base, Uwe

    2011-06-01

    A new approach to optimize the parameters of a gradient-based optical flow model using a parallel genetic algorithm (GA) is proposed. The main characteristics of the optical flow algorithm are its bio-inspiration and robustness against contrast, static patterns and noise, besides working consistently with several optical illusions where other algorithms fail. This model depends on many parameters which conform the number of channels, the orientations required, the length and shape of the kernel functions used in the convolution stage, among many more. The GA is used to find a set of parameters which improve the accuracy of the optical flow on inputs where the ground-truth data is available. This set of parameters helps to understand which of them are better suited for each type of inputs and can be used to estimate the parameters of the optical flow algorithm when used with videos that share similar characteristics. The proposed implementation takes into account the embarrassingly parallel nature of the GA and uses the OpenMP Application Programming Interface (API) to speedup the process of estimating an optimal set of parameters. The information obtained in this work can be used to dynamically reconfigure systems, with potential applications in robotics, medical imaging and tracking.

  8. Flexible ligand docking using a genetic algorithm

    Science.gov (United States)

    Oshiro, C. M.; Kuntz, I. D.; Dixon, J. Scott

    1995-04-01

    Two computational techniques have been developed to explore the orientational and conformational space of a flexible ligand within an enzyme. Both methods use the Genetic Algorithm (GA) to generate conformationally flexible ligands in conjunction with algorithms from the DOCK suite of programs to characterize the receptor site. The methods are applied to three enzyme-ligand complexes: dihydrofolate reductase-methotrexate, thymidylate synthase-phenolpthalein and HIV protease-thioketal haloperidol. Conformations and orientations close to the crystallographically determined structures are obtained, as well as alternative structures with low energy. The potential for the GA method to screen a database of compounds is also examined. A collection of ligands is evaluated simultaneously, rather than docking the ligands individually into the enzyme.

  9. Grouping genetic algorithms advances and applications

    CERN Document Server

    Mutingi, Michael

    2017-01-01

    This book presents advances and innovations in grouping genetic algorithms, enriched with new and unique heuristic optimization techniques. These algorithms are specially designed for solving industrial grouping problems where system entities are to be partitioned or clustered into efficient groups according to a set of guiding decision criteria. Examples of such problems are: vehicle routing problems, team formation problems, timetabling problems, assembly line balancing, group maintenance planning, modular design, and task assignment. A wide range of industrial grouping problems, drawn from diverse fields such as logistics, supply chain management, project management, manufacturing systems, engineering design and healthcare, are presented. Typical complex industrial grouping problems, with multiple decision criteria and constraints, are clearly described using illustrative diagrams and formulations. The problems are mapped into a common group structure that can conveniently be used as an input scheme to spe...

  10. Genetic Algorithm Tuned Fuzzy Logic for Gliding Return Trajectories

    Science.gov (United States)

    Burchett, Bradley T.

    2003-01-01

    The problem of designing and flying a trajectory for successful recovery of a reusable launch vehicle is tackled using fuzzy logic control with genetic algorithm optimization. The plant is approximated by a simplified three degree of freedom non-linear model. A baseline trajectory design and guidance algorithm consisting of several Mamdani type fuzzy controllers is tuned using a simple genetic algorithm. Preliminary results show that the performance of the overall system is shown to improve with genetic algorithm tuning.

  11. Optimisation of nonlinear motion cueing algorithm based on genetic algorithm

    Science.gov (United States)

    Asadi, Houshyar; Mohamed, Shady; Rahim Zadeh, Delpak; Nahavandi, Saeid

    2015-04-01

    Motion cueing algorithms (MCAs) are playing a significant role in driving simulators, aiming to deliver the most accurate human sensation to the simulator drivers compared with a real vehicle driver, without exceeding the physical limitations of the simulator. This paper provides the optimisation design of an MCA for a vehicle simulator, in order to find the most suitable washout algorithm parameters, while respecting all motion platform physical limitations, and minimising human perception error between real and simulator driver. One of the main limitations of the classical washout filters is that it is attuned by the worst-case scenario tuning method. This is based on trial and error, and is effected by driving and programmers experience, making this the most significant obstacle to full motion platform utilisation. This leads to inflexibility of the structure, production of false cues and makes the resulting simulator fail to suit all circumstances. In addition, the classical method does not take minimisation of human perception error and physical constraints into account. Production of motion cues and the impact of different parameters of classical washout filters on motion cues remain inaccessible for designers for this reason. The aim of this paper is to provide an optimisation method for tuning the MCA parameters, based on nonlinear filtering and genetic algorithms. This is done by taking vestibular sensation error into account between real and simulated cases, as well as main dynamic limitations, tilt coordination and correlation coefficient. Three additional compensatory linear blocks are integrated into the MCA, to be tuned in order to modify the performance of the filters successfully. The proposed optimised MCA is implemented in MATLAB/Simulink software packages. The results generated using the proposed method show increased performance in terms of human sensation, reference shape tracking and exploiting the platform more efficiently without reaching

  12. Genetic Algorithms Principles Towards Hidden Markov Model

    Directory of Open Access Journals (Sweden)

    Nabil M. Hewahi

    2011-10-01

    Full Text Available In this paper we propose a general approach based on Genetic Algorithms (GAs to evolve Hidden Markov Models (HMM. The problem appears when experts assign probability values for HMM, they use only some limited inputs. The assigned probability values might not be accurate to serve in other cases related to the same domain. We introduce an approach based on GAs to find
    out the suitable probability values for the HMM to be mostly correct in more cases than what have been used to assign the probability values.

  13. Using Case Retrieval to Seed Genetic Algorithms

    OpenAIRE

    Oman, Stephen; Cunningham, Padraig

    1997-01-01

    In this paper we evaluate the usefulness of seeding genetic algorithms (GAs) from a case-base. This is motivated by the expectation that the seeding will speed up the GA by starting the search in promising regions of the search space. We evaluate this case-based seeding on popular GA solutions to the Travelling Salesman Problem (TSP) and the Job-Shop Scheduling Problem (JSSP). We find that seeding works very well with the TSP but poorly with the JSSP. We have discovered that th...

  14. Comparison of genetic algorithms with conjugate gradient methods

    Science.gov (United States)

    Bosworth, J. L.; Foo, N. Y.; Zeigler, B. P.

    1972-01-01

    Genetic algorithms for mathematical function optimization are modeled on search strategies employed in natural adaptation. Comparisons of genetic algorithms with conjugate gradient methods, which were made on an IBM 1800 digital computer, show that genetic algorithms display superior performance over gradient methods for functions which are poorly behaved mathematically, for multimodal functions, and for functions obscured by additive random noise. Genetic methods offer performance comparable to gradient methods for many of the standard functions.

  15. Rational function optimization using genetic algorithms

    Science.gov (United States)

    Valadan Zoej, M. J.; Mokhtarzade, M.; Mansourian, A.; Ebadi, H.; Sadeghian, S.

    2007-12-01

    In the absence of either satellite ephemeris information or camera model, rational functions are introduced by many investigators as mathematical model for image to ground coordinate system transformation. The dependency of this method on many ground control points (GCPs), numerical complexity, particularly terms selection, can be regarded as the most known disadvantages of rational functions. This paper presents a mathematical solution to overcome these problems. Genetic algorithms are used as an intelligent method for optimum rational function terms selection. The results from an experimental test carried out over a test field in Iran are presented as utilizing an IKONOS Geo image. Different numbers of GCPs are fed through a variety of genetic algorithms (GAs) with different control parameter settings. Some initial constraints are introduced to make the process stable and fast. The residual errors at independent check points proved that sub-pixel accuracies can be achieved even when only seven and five GCPs are used. GAs could select rational function terms in such a way that numerical problems are avoided without the need to normalize image and ground coordinates.

  16. Explicit filtering of building blocks for genetic algorithms

    NARCIS (Netherlands)

    C.H.M. van Kemenade

    1996-01-01

    textabstractGenetic algorithms are often applied to building block problems. We have developed a simple filtering algorithm that can locate building blocks within a bit-string, and does not make assumptions regarding the linkage of the bits. A comparison between the filtering algorithm and genetic

  17. An improved genetic algorithm with dynamic topology

    Science.gov (United States)

    Cai, Kai-Quan; Tang, Yan-Wu; Zhang, Xue-Jun; Guan, Xiang-Min

    2016-12-01

    The genetic algorithm (GA) is a nature-inspired evolutionary algorithm to find optima in search space via the interaction of individuals. Recently, researchers demonstrated that the interaction topology plays an important role in information exchange among individuals of evolutionary algorithm. In this paper, we investigate the effect of different network topologies adopted to represent the interaction structures. It is found that GA with a high-density topology ends up more likely with an unsatisfactory solution, contrarily, a low-density topology can impede convergence. Consequently, we propose an improved GA with dynamic topology, named DT-GA, in which the topology structure varies dynamically along with the fitness evolution. Several experiments executed with 15 well-known test functions have illustrated that DT-GA outperforms other test GAs for making a balance of convergence speed and optimum quality. Our work may have implications in the combination of complex networks and computational intelligence. Project supported by the National Natural Science Foundation for Young Scientists of China (Grant No. 61401011), the National Key Technologies R & D Program of China (Grant No. 2015BAG15B01), and the National Natural Science Foundation of China (Grant No. U1533119).

  18. WWW portal usage analysis using genetic algorithms

    Directory of Open Access Journals (Sweden)

    Ondřej Popelka

    2009-01-01

    Full Text Available The article proposes a new method suitable for advanced analysis of web portal visits. This is part of retrieving information and knowledge from web usage data (web usage mining. Such information is necessary in order to gain better insight into visitor’s needs and generally consumer behaviour. By le­ve­ra­ging this information a company can optimize the organization of its internet presentations and offer a better end-user experience. The proposed approach is using Grammatical evolution which is computational method based on genetic algorithms. Grammatical evolution is using a context-free grammar in order to generate the solution in arbitrary reusable form. This allows us to describe visitors’ behaviour in different manners depending on desired further processing. In this article we use description with a procedural programming language. Web server access log files are used as source data.The extraction of behaviour patterns can currently be solved using statistical analysis – specifically sequential analysis based methods. Our objective is to develop an alternative algorithm.The article further describes the basic algorithms of two-level grammatical evolution; this involves basic Grammatical Evolution and Differential Evolution, which forms the second phase of the computation. Grammatical evolution is used to generate the basic structure of the solution – in form of a part of application code. Differential evolution is used to find optimal parameters for this solution – the specific pages visited by a random visitor. The grammar used to conduct experiments is described along with explanations of the links to the actual implementation of the algorithm. Furthermore the fitness function is described and reasons which yield to its’ current shape. Finally the process of analyzing and filtering the raw input data is described as it is vital part in obtaining reasonable results.

  19. Multidisciplinary design optimization using genetic algorithms

    Science.gov (United States)

    Unal, Resit

    1994-01-01

    Multidisciplinary design optimization (MDO) is an important step in the conceptual design and evaluation of launch vehicles since it can have a significant impact on performance and life cycle cost. The objective is to search the system design space to determine values of design variables that optimize the performance characteristic subject to system constraints. Gradient-based optimization routines have been used extensively for aerospace design optimization. However, one limitation of gradient based optimizers is their need for gradient information. Therefore, design problems which include discrete variables can not be studied. Such problems are common in launch vehicle design. For example, the number of engines and material choices must be integer values or assume only a few discrete values. In this study, genetic algorithms are investigated as an approach to MDO problems involving discrete variables and discontinuous domains. Optimization by genetic algorithms (GA) uses a search procedure which is fundamentally different from those gradient based methods. Genetic algorithms seek to find good solutions in an efficient and timely manner rather than finding the best solution. GA are designed to mimic evolutionary selection. A population of candidate designs is evaluated at each iteration, and each individual's probability of reproduction (existence in the next generation) depends on its fitness value (related to the value of the objective function). Progress toward the optimum is achieved by the crossover and mutation operations. GA is attractive since it uses only objective function values in the search process, so gradient calculations are avoided. Hence, GA are able to deal with discrete variables. Studies report success in the use of GA for aircraft design optimization studies, trajectory analysis, space structure design and control systems design. In these studies reliable convergence was achieved, but the number of function evaluations was large compared

  20. Study on Multi-stage Logistics System Design Problem with Inventory Considering Demand Change by Hybrid Genetic Algorithm

    Science.gov (United States)

    Inoue, Hisaki; Gen, Mitsuo

    The logistics model used in this study is 3-stage model employed by an automobile company, which aims to solve traffic problems at a total minimum cost. Recently, research on the metaheuristics method has advanced as an approximate means for solving optimization problems like this model. These problems can be solved using various methods such as the genetic algorithm (GA), simulated annealing, and tabu search. GA is superior in robustness and adjustability toward a change in the structure of these problems. However, GA has a disadvantage in that it has a slightly inefficient search performance because it carries out a multi-point search. A hybrid GA that combines another method is attracting considerable attention since it can compensate for a fault to a partial solution that early convergence gives a bad influence on a result. In this study, we propose a novel hybrid random key-based GA(h-rkGA) that combines local search and parameter tuning of crossover rate and mutation rate; h-rkGA is an improved version of the random key-based GA (rk-GA). We attempted comparative experiments with spanning tree-based GA, priority based GA and random key-based GA. Further, we attempted comparative experiments with “h-GA by only local search” and “h-GA by only parameter tuning”. We reported the effectiveness of the proposed method on the basis of the results of these experiments.

  1. New Algorithm of Automatic Complex Password Generator Employing Genetic Algorithm

    Directory of Open Access Journals (Sweden)

    Sura Jasim Mohammed

    2018-01-01

    Full Text Available Due to the occurred increasing in information sharing, internet popularization, E-commerce transactions, and data transferring, security and authenticity become an important and necessary subject. In this paper an automated schema was proposed to generate a strong and complex password which is based on entering initial data such as text (meaningful and simple information or not, with the concept of encoding it, then employing the Genetic Algorithm by using its operations crossover and mutation to generated different data from the entered one. The generated password is non-guessable and can be used in many and different applications and internet services like social networks, secured system, distributed systems, and online services. The proposed password generator achieved diffusion, randomness, and confusions, which are very necessary, required and targeted in the resulted password, in addition to the notice that the length of the generated password differs from the length of initial data, and any simple changing and modification in the initial data produces more and clear modification in the generated password. The proposed work was done using visual basic programing language.

  2. A novel progressively swarmed mixed integer genetic algorithm for ...

    African Journals Online (AJOL)

    MIGA) which inherits the advantages of binary and real coded Genetic Algorithm approach. The proposed algorithm is applied for the conventional generation cost minimization Optimal Power Flow (OPF) problem and for the Security ...

  3. An Indirect Genetic Algorithm for a Nurse Scheduling Problem

    OpenAIRE

    Aickelin, Uwe; Dowsland, Kathryn

    2008-01-01

    This paper describes a Genetic Algorithms approach to a manpower-scheduling problem arising at a major UK hospital. Although Genetic Algorithms have been successfully used for similar problems in the past, they always had to overcome the limitations of the classical Genetic Algorithms paradigm in handling the conflict between objectives and constraints. The approach taken here is to use an indirect coding based on permutations of the nurses, and a heuristic decoder that builds schedules from ...

  4. Training product unit neural networks with genetic algorithms

    Science.gov (United States)

    Janson, D. J.; Frenzel, J. F.; Thelen, D. C.

    1991-01-01

    The training of product neural networks using genetic algorithms is discussed. Two unusual neural network techniques are combined; product units are employed instead of the traditional summing units and genetic algorithms train the network rather than backpropagation. As an example, a neural netork is trained to calculate the optimum width of transistors in a CMOS switch. It is shown how local minima affect the performance of a genetic algorithm, and one method of overcoming this is presented.

  5. Solving the Dial-a-Ride Problem using Genetic algorithms

    DEFF Research Database (Denmark)

    Bergvinsdottir, Kristin Berg; Larsen, Jesper; Jørgensen, Rene Munk

    service level constraints (Quality of Service). In this paper we present a genetic algorithm for solving the DARP. The algorithm is based on the classical cluster-first route-second approach, where it alternates between assigning customers to vehicles using a genetic algorithm and solving independent...... routing problems for the vehicles using a routing heuristic. The algorithm is implemented in Java and tested on publicly available data sets....

  6. Composite Structure Optimization with Genetic Algorithm

    Science.gov (United States)

    Deslandes, Olivier

    2014-06-01

    In the frame of optimization studies in CNES launcher directorate structure, thermic and material department, the need of an optimization tool based on metaheuristic and finite element models for composite structural dimensioning was underlined.Indeed, composite structures need complex optimization methodologies in order to be really compared to metallic structures with regard to mass, static strength and stiffness constraints (metallic structures using optimization methods better known).After some bibliography research, the use of a genetic algorithm coupled with design of experiment to generate the initial population was chosen. Academic functions were used to validate the optimization process and then it was applied to an industrial study aiming to optimize an interstage skirt with regard to its mass, stiffness and stability (global buckling).

  7. Warehouse Optimization Model Based on Genetic Algorithm

    Directory of Open Access Journals (Sweden)

    Guofeng Qin

    2013-01-01

    Full Text Available This paper takes Bao Steel logistics automated warehouse system as an example. The premise is to maintain the focus of the shelf below half of the height of the shelf. As a result, the cost time of getting or putting goods on the shelf is reduced, and the distance of the same kind of goods is also reduced. Construct a multiobjective optimization model, using genetic algorithm to optimize problem. At last, we get a local optimal solution. Before optimization, the average cost time of getting or putting goods is 4.52996 s, and the average distance of the same kinds of goods is 2.35318 m. After optimization, the average cost time is 4.28859 s, and the average distance is 1.97366 m. After analysis, we can draw the conclusion that this model can improve the efficiency of cargo storage.

  8. PDE Nozzle Optimization Using a Genetic Algorithm

    Science.gov (United States)

    Billings, Dana; Turner, James E. (Technical Monitor)

    2000-01-01

    Genetic algorithms, which simulate evolution in natural systems, have been used to find solutions to optimization problems that seem intractable to standard approaches. In this study, the feasibility of using a GA to find an optimum, fixed profile nozzle for a pulse detonation engine (PDE) is demonstrated. The objective was to maximize impulse during the detonation wave passage and blow-down phases of operation. Impulse of each profile variant was obtained by using the CFD code Mozart/2.0 to simulate the transient flow. After 7 generations, the method has identified a nozzle profile that certainly is a candidate for optimum solution. The constraints on the generality of this possible solution remain to be clarified.

  9. Genetic Algorithm Based Microscale Vehicle Emissions Modelling

    Directory of Open Access Journals (Sweden)

    Sicong Zhu

    2015-01-01

    Full Text Available There is a need to match emission estimations accuracy with the outputs of transport models. The overall error rate in long-term traffic forecasts resulting from strategic transport models is likely to be significant. Microsimulation models, whilst high-resolution in nature, may have similar measurement errors if they use the outputs of strategic models to obtain traffic demand predictions. At the microlevel, this paper discusses the limitations of existing emissions estimation approaches. Emission models for predicting emission pollutants other than CO2 are proposed. A genetic algorithm approach is adopted to select the predicting variables for the black box model. The approach is capable of solving combinatorial optimization problems. Overall, the emission prediction results reveal that the proposed new models outperform conventional equations in terms of accuracy and robustness.

  10. Optimization of PID Controllers Using Ant Colony and Genetic Algorithms

    CERN Document Server

    Ünal, Muhammet; Topuz, Vedat; Erdal, Hasan

    2013-01-01

    Artificial neural networks, genetic algorithms and the ant colony optimization algorithm have become a highly effective tool for solving hard optimization problems. As their popularity has increased, applications of these algorithms have grown in more than equal measure. While many of the books available on these subjects only provide a cursory discussion of theory, the present book gives special emphasis to the theoretical background that is behind these algorithms and their applications. Moreover, this book introduces a novel real time control algorithm, that uses genetic algorithm and ant colony optimization algorithms for optimizing PID controller parameters. In general, the present book represents a solid survey on artificial neural networks, genetic algorithms and the ant colony optimization algorithm and introduces novel practical elements related to the application of these methods to  process system control.

  11. A Cluster-Based Orthogonal Multi-Objective Genetic Algorithm

    Science.gov (United States)

    Zhu, Jiankai; Dai, Guangming; Mo, Li

    Multi-objective genetic algorithm is proved to be suitable for solving multi-objective optimization problems. However, it is usually very hard to balance the convergence and diversity of a multi-objective genetic algorithm. This paper introduces a new algorithm, with both good convergence and diversity based on clustering method and multi-parent crossover operator. Meanwhile, an initial population is generated by orthogonal design to enhance the search effort of the algorithm. The experimental results on a number of test problems indicate the good performance of the Cluster-Based Orthogonal Multi-Objective Genetic Algorithm.

  12. Using Genetic Algorithms for Building Metrics of Collaborative Systems

    Directory of Open Access Journals (Sweden)

    Cristian CIUREA

    2011-01-01

    Full Text Available he paper objective is to reveal the importance of genetic algorithms in building robust metrics of collaborative systems. The main types of collaborative systems in economy are presented and some characteristics of genetic algorithms are described. A genetic algorithm was implemented in order to determine the local maximum and minimum points of the relative complexity function associated to a collaborative banking system. The intelligent collaborative systems based on genetic algorithms, representing the new generation of collaborative systems, are analyzed and the implementation of auto-adaptive interfaces in a banking application is described.

  13. Improvement of ECM Techniques through Implementation of a Genetic Algorithm

    National Research Council Canada - National Science Library

    Townsend, James D

    2008-01-01

    This research effort develops the necessary interfaces between the radar signal processing components and an optimization routine, such as genetic algorithms, to develop Electronic Countermeasure (ECM...

  14. A novel mating approach for genetic algorithms.

    Science.gov (United States)

    Galán, Severino F; Mengshoel, Ole J; Pinter, Rafael

    2013-01-01

    Genetic algorithms typically use crossover, which relies on mating a set of selected parents. As part of crossover, random mating is often carried out. A novel approach to parent mating is presented in this work. Our novel approach can be applied in combination with a traditional similarity-based criterion to measure distance between individuals or with a fitness-based criterion. We introduce a parameter called the mating index that allows different mating strategies to be developed within a uniform framework: an exploitative strategy called best-first, an explorative strategy called best-last, and an adaptive strategy called self-adaptive. Self-adaptive mating is defined in the context of the novel algorithm, and aims to achieve a balance between exploitation and exploration in a domain-independent manner. The present work formally defines the novel mating approach, analyzes its behavior, and conducts an extensive experimental study to quantitatively determine its benefits. In the domain of real function optimization, the experiments show that, as the degree of multimodality of the function at hand grows, increasing the mating index improves performance. In the case of the self-adaptive mating strategy, the experiments give strong results for several case studies.

  15. Spacecraft Attitude Maneuver Planning Using Genetic Algorithms

    Science.gov (United States)

    Kornfeld, Richard P.

    2004-01-01

    A key enabling technology that leads to greater spacecraft autonomy is the capability to autonomously and optimally slew the spacecraft from and to different attitudes while operating under a number of celestial and dynamic constraints. The task of finding an attitude trajectory that meets all the constraints is a formidable one, in particular for orbiting or fly-by spacecraft where the constraints and initial and final conditions are of time-varying nature. This approach for attitude path planning makes full use of a priori constraint knowledge and is computationally tractable enough to be executed onboard a spacecraft. The approach is based on incorporating the constraints into a cost function and using a Genetic Algorithm to iteratively search for and optimize the solution. This results in a directed random search that explores a large part of the solution space while maintaining the knowledge of good solutions from iteration to iteration. A solution obtained this way may be used as is or as an initial solution to initialize additional deterministic optimization algorithms. A number of representative case examples for time-fixed and time-varying conditions yielded search times that are typically on the order of minutes, thus demonstrating the viability of this method. This approach is applicable to all deep space and planet Earth missions requiring greater spacecraft autonomy, and greatly facilitates navigation and science observation planning.

  16. Dynamic airspace configuration by genetic algorithm

    Directory of Open Access Journals (Sweden)

    Marina Sergeeva

    2017-06-01

    Full Text Available With the continuous air traffic growth and limits of resources, there is a need for reducing the congestion of the airspace systems. Nowadays, several projects are launched, aimed at modernizing the global air transportation system and air traffic management. In recent years, special interest has been paid to the solution of the dynamic airspace configuration problem. Airspace sector configurations need to be dynamically adjusted to provide maximum efficiency and flexibility in response to changing weather and traffic conditions. The main objective of this work is to automatically adapt the airspace configurations according to the evolution of traffic. In order to reach this objective, the airspace is considered to be divided into predefined 3D airspace blocks which have to be grouped or ungrouped depending on the traffic situation. The airspace structure is represented as a graph and each airspace configuration is created using a graph partitioning technique. We optimize airspace configurations using a genetic algorithm. The developed algorithm generates a sequence of sector configurations for one day of operation with the minimized controller workload. The overall methodology is implemented and successfully tested with air traffic data taken for one day and for several different airspace control areas of Europe.

  17. Optimizing doped libraries by using genetic algorithms

    Science.gov (United States)

    Tomandl, Dirk; Schober, Andreas; Schwienhorst, Andreas

    1997-01-01

    The insertion of random sequences into protein-encoding genes in combination with biologicalselection techniques has become a valuable tool in the design of molecules that have usefuland possibly novel properties. By employing highly effective screening protocols, a functionaland unique structure that had not been anticipated can be distinguished among a hugecollection of inactive molecules that together represent all possible amino acid combinations.This technique is severely limited by its restriction to a library of manageable size. Oneapproach for limiting the size of a mutant library relies on `doping schemes', where subsetsof amino acids are generated that reveal only certain combinations of amino acids in a proteinsequence. Three mononucleotide mixtures for each codon concerned must be designed, suchthat the resulting codons that are assembled during chemical gene synthesis represent thedesired amino acid mixture on the level of the translated protein. In this paper we present adoping algorithm that `reverse translates' a desired mixture of certain amino acids into threemixtures of mononucleotides. The algorithm is designed to optimally bias these mixturestowards the codons of choice. This approach combines a genetic algorithm with localoptimization strategies based on the downhill simplex method. Disparate relativerepresentations of all amino acids (and stop codons) within a target set can be generated.Optional weighing factors are employed to emphasize the frequencies of certain amino acidsand their codon usage, and to compensate for reaction rates of different mononucleotidebuilding blocks (synthons) during chemical DNA synthesis. The effect of statistical errors thataccompany an experimental realization of calculated nucleotide mixtures on the generatedmixtures of amino acids is simulated. These simulations show that the robustness of differentoptima with respect to small deviations from calculated values depends on their concomitantfitness. Furthermore

  18. Particle swarm optimization with random keys applied to the nuclear reactor reload problem

    Energy Technology Data Exchange (ETDEWEB)

    Meneses, Anderson Alvarenga de Moura [Universidade Federal do Rio de Janeiro (UFRJ), RJ (Brazil). Coordenacao dos Programas de Pos-graduacao de Engenharia (COPPE). Programa de Engenharia Nuclear; Fundacao Educacional de Macae (FUNEMAC), RJ (Brazil). Faculdade Professor Miguel Angelo da Silva Santos; Machado, Marcelo Dornellas; Medeiros, Jose Antonio Carlos Canedo; Schirru, Roberto [Universidade Federal do Rio de Janeiro (UFRJ), RJ (Brazil). Coordenacao dos Programas de Pos-graduacao de Engenharia (COPPE). Programa de Engenharia Nuclear]. E-mails: ameneses@con.ufrj.br; marcelo@lmp.ufrj.br; canedo@lmp.ufrj.br; schirru@lmp.ufrj.br

    2007-07-01

    In 1995, Kennedy and Eberhart presented the Particle Swarm Optimization (PSO), an Artificial Intelligence metaheuristic technique to optimize non-linear continuous functions. The concept of Swarm Intelligence is based on the socials aspects of intelligence, it means, the ability of individuals to learn with their own experience in a group as well as to take advantage of the performance of other individuals. Some PSO models for discrete search spaces have been developed for combinatorial optimization, although none of them presented satisfactory results to optimize a combinatorial problem as the nuclear reactor fuel reloading problem (NRFRP). In this sense, we developed the Particle Swarm Optimization with Random Keys (PSORK) in previous research to solve Combinatorial Problems. Experiences demonstrated that PSORK performed comparable to or better than other techniques. Thus, PSORK metaheuristic is being applied in optimization studies of the NRFRP for Angra 1 Nuclear Power Plant. Results will be compared with Genetic Algorithms and the manual method provided by a specialist. In this experience, the problem is being modeled for an eight-core symmetry and three-dimensional geometry, aiming at the minimization of the Nuclear Enthalpy Power Peaking Factor as well as the maximization of the cycle length. (author)

  19. Solving Hub Network Problem Using Genetic Algorithm

    Directory of Open Access Journals (Sweden)

    Mursyid Hasan Basri

    2012-01-01

    non-linearity, so there is no guarantee to find the optimal solution. Moreover, it has generated a great number of variables. Therefore, a heuristic method is required to find near optimal solution with reasonable computation time. For this reason, a genetic algorithm (GA-based procedure is proposed. The proposed procedure then is applied to the same problem as discussed in the basic model. The results indicated that there is significant improvement on hub locations. Flows are successfully consolidated to several big ports as expected. With regards to spoke allocations, however, spokes are not fairly allocated.Keywords: Hub and Spoke Model; Marine Transportation; Genetic Algorithm

  20. A Test of Genetic Algorithms in Relevance Feedback.

    Science.gov (United States)

    Lopez-Pujalte, Cristina; Guerrero Bote, Vicente P.; Moya Anegon, Felix de

    2002-01-01

    Discussion of information retrieval, query optimization techniques, and relevance feedback focuses on genetic algorithms, which are derived from artificial intelligence techniques. Describes an evaluation of different genetic algorithms using a residual collection method and compares results with the Ide dec-hi method (Salton and Buckley, 1990…

  1. Modeling of genetic algorithms with a finite population

    NARCIS (Netherlands)

    C.H.M. van Kemenade

    1997-01-01

    textabstractCross-competition between non-overlapping building blocks can strongly influence the performance of evolutionary algorithms. The choice of the selection scheme can have a strong influence on the performance of a genetic algorithm. This paper describes a number of different genetic

  2. Niching genetic algorithms for optimization in electromagnetics - I. Fundamentals

    OpenAIRE

    Sareni, Bruno; Krähenbühl, Laurent; Nicolas, Alain

    1998-01-01

    Niching methods extend genetic algorithms and permit the investigation of multiple optimal solutions in the search space. In this paper, we review and discuss various strategies of niching for optimization in electromagnetics. Traditional mathematical problems and an electromagnetic benchmark are solved using niching genetic algorithms to show their interest in real world optimization.

  3. On the Analysis of the Simple Genetic Algorithm

    DEFF Research Database (Denmark)

    Oliveto, Pietro S.; Witt, Carsten

    2012-01-01

    For many years it has been a challenge to analyze the time complexity of Genetic Algorithms (GAs) using stochastic selection together with crossover and mutation. This paper presents a rigorous runtime analysis of the well-known Simple Genetic Algorithm (SGA) for OneMax. It is proved that the SGA...

  4. On the runtime analysis of the Simple Genetic Algorithm

    DEFF Research Database (Denmark)

    Oliveto, Pietro S.; Witt, Carsten

    2014-01-01

    For many years it has been a challenge to analyze the time complexity of Genetic Algorithms (GAs) using stochastic selection together with crossover and mutation. This paper presents a rigorous runtime analysis of the well-known Simple Genetic Algorithm (SGA) for OneMax. It is proved that the SGA...

  5. Application of a Genetic Algorithm to Nearest Neighbour Classification

    NARCIS (Netherlands)

    Simkin, S.; Verwaart, D.; Vrolijk, H.C.J.

    2005-01-01

    This paper describes the application of a genetic algorithm to nearest-neighbour based imputation of sample data into a census data dataset. The genetic algorithm optimises the selection and weights of variables used for measuring distance. The results show that the measure of fit can be improved by

  6. Genetic algorithms principles and perspectives : a guide to GA theory

    CERN Document Server

    Reeves, Colin R; Reeves, Colin R

    2002-01-01

    Genetic Algorithms (GAs) have become a highly effective tool for solving hard optimization problems. This text provides a survey of some important theoretical contributions, many of which have been proposed and developed in the Foundations of Genetic Algorithms series of workshops.

  7. Using Genetic Algorithms in Secured Business Intelligence Mobile Applications

    Directory of Open Access Journals (Sweden)

    Silvia TRIF

    2011-01-01

    Full Text Available The paper aims to assess the use of genetic algorithms for training neural networks used in secured Business Intelligence Mobile Applications. A comparison is made between classic back-propagation method and a genetic algorithm based training. The design of these algorithms is presented. A comparative study is realized for determining the better way of training neural networks, from the point of view of time and memory usage. The results show that genetic algorithms based training offer better performance and memory usage than back-propagation and they are fit to be implemented on mobile devices.

  8. Advanced optimization of permanent magnet wigglers using a genetic algorithm

    Energy Technology Data Exchange (ETDEWEB)

    Hajima, Ryoichi [Univ. of Tokyo (Japan)

    1995-12-31

    In permanent magnet wigglers, magnetic imperfection of each magnet piece causes field error. This field error can be reduced or compensated by sorting magnet pieces in proper order. We showed a genetic algorithm has good property for this sorting scheme. In this paper, this optimization scheme is applied to the case of permanent magnets which have errors in the direction of field. The result shows the genetic algorithm is superior to other algorithms.

  9. Closed Loop System Identification with Genetic Algorithms

    Science.gov (United States)

    Whorton, Mark S.

    2004-01-01

    High performance control design for a flexible space structure is challenging since high fidelity plant models are di.cult to obtain a priori. Uncertainty in the control design models typically require a very robust, low performance control design which must be tuned on-orbit to achieve the required performance. Closed loop system identi.cation is often required to obtain a multivariable open loop plant model based on closed-loop response data. In order to provide an accurate initial plant model to guarantee convergence for standard local optimization methods, this paper presents a global parameter optimization method using genetic algorithms. A minimal representation of the state space dynamics is employed to mitigate the non-uniqueness and over-parameterization of general state space realizations. This control-relevant system identi.cation procedure stresses the joint nature of the system identi.cation and control design problem by seeking to obtain a model that minimizes the di.erence between the predicted and actual closed-loop performance.

  10. Lunar Habitat Optimization Using Genetic Algorithms

    Science.gov (United States)

    SanScoucie, M. P.; Hull, P. V.; Tinker, M. L.; Dozier, G. V.

    2007-01-01

    Long-duration surface missions to the Moon and Mars will require bases to accommodate habitats for the astronauts. Transporting the materials and equipment required to build the necessary habitats is costly and difficult. The materials chosen for the habitat walls play a direct role in protection against each of the mentioned hazards. Choosing the best materials, their configuration, and the amount required is extremely difficult due to the immense size of the design region. Clearly, an optimization method is warranted for habitat wall design. Standard optimization techniques are not suitable for problems with such large search spaces; therefore, a habitat wall design tool utilizing genetic algorithms (GAs) has been developed. GAs use a "survival of the fittest" philosophy where the most fit individuals are more likely to survive and reproduce. This habitat design optimization tool is a multiobjective formulation of up-mass, heat loss, structural analysis, meteoroid impact protection, and radiation protection. This Technical Publication presents the research and development of this tool as well as a technique for finding the optimal GA search parameters.

  11. Hydro Power Reservoir Aggregation via Genetic Algorithms

    Directory of Open Access Journals (Sweden)

    Markus Löschenbrand

    2017-12-01

    Full Text Available Electrical power systems with a high share of hydro power in their generation portfolio tend to display distinct behavior. Low generation cost and the possibility of peak shaving create a high amount of flexibility. However, stochastic influences such as precipitation and external market effects create uncertainty and thus establish a wide range of potential outcomes. Therefore, optimal generation scheduling is a key factor to successful operation of hydro power dominated systems. This paper aims to bridge the gap between scheduling on large-scale (e.g., national and small scale (e.g., a single river basin levels, by applying a multi-objective master/sub-problem framework supported by genetic algorithms. A real-life case study from southern Norway is used to assess the validity of the method and give a proof of concept. The introduced method can be applied to efficiently integrate complex stochastic sub-models into Virtual Power Plants and thus reduce the computational complexity of large-scale models whilst minimizing the loss of information.

  12. OPC recipe optimization using genetic algorithm

    Science.gov (United States)

    Asthana, Abhishek; Wilkinson, Bill; Power, Dave

    2016-03-01

    Optimization of OPC recipes is not trivial due to multiple parameters that need tuning and their correlation. Usually, no standard methodologies exist for choosing the initial recipe settings, and in the keyword development phase, parameters are chosen either based on previous learning, vendor recommendations, or to resolve specific problems on particular special constructs. Such approaches fail to holistically quantify the effects of parameters on other or possible new designs, and to an extent are based on the keyword developer's intuition. In addition, when a quick fix is needed for a new design, numerous customization statements are added to the recipe, which make it more complex. The present work demonstrates the application of Genetic Algorithm (GA) technique for optimizing OPC recipes. GA is a search technique that mimics Darwinian natural selection and has applications in various science and engineering disciplines. In this case, GA search heuristic is applied to two problems: (a) an overall OPC recipe optimization with respect to selected parameters and, (b) application of GA to improve printing and via coverage at line end geometries. As will be demonstrated, the optimized recipe significantly reduced the number of ORC violations for case (a). For case (b) line end for various features showed significant printing and filling improvement.

  13. Mobile robot dynamic path planning based on improved genetic algorithm

    Science.gov (United States)

    Wang, Yong; Zhou, Heng; Wang, Ying

    2017-08-01

    In dynamic unknown environment, the dynamic path planning of mobile robots is a difficult problem. In this paper, a dynamic path planning method based on genetic algorithm is proposed, and a reward value model is designed to estimate the probability of dynamic obstacles on the path, and the reward value function is applied to the genetic algorithm. Unique coding techniques reduce the computational complexity of the algorithm. The fitness function of the genetic algorithm fully considers three factors: the security of the path, the shortest distance of the path and the reward value of the path. The simulation results show that the proposed genetic algorithm is efficient in all kinds of complex dynamic environments.

  14. Optimization of Pressurizer Based on Genetic-Simplex Algorithm

    Energy Technology Data Exchange (ETDEWEB)

    Wang, Cheng; Yan, Chang Qi; Wang, Jian Jun [Harbin Engineering University, Harbin (China)

    2014-08-15

    Pressurizer is one of key components in nuclear power system. It's important to control the dimension in the design of pressurizer through optimization techniques. In this work, a mathematic model of a vertical electric heating pressurizer was established. A new Genetic-Simplex Algorithm (GSA) that combines genetic algorithm and simplex algorithm was developed to enhance the searching ability, and the comparison among modified and original algorithms is conducted by calculating the benchmark function. Furthermore, the optimization design of pressurizer, taking minimization of volume and net weight as objectives, was carried out considering thermal-hydraulic and geometric constraints through GSA. The results indicate that the mathematical model is agreeable for the pressurizer and the new algorithm is more effective than the traditional genetic algorithm. The optimization design shows obvious validity and can provide guidance for real engineering design.

  15. Restart-Based Genetic Algorithm for the Quadratic Assignment Problem

    Science.gov (United States)

    Misevicius, Alfonsas

    The power of genetic algorithms (GAs) has been demonstrated for various domains of the computer science, including combinatorial optimization. In this paper, we propose a new conceptual modification of the genetic algorithm entitled a "restart-based genetic algorithm" (RGA). An effective implementation of RGA for a well-known combinatorial optimization problem, the quadratic assignment problem (QAP), is discussed. The results obtained from the computational experiments on the QAP instances from the publicly available library QAPLIB show excellent performance of RGA. This is especially true for the real-life like QAPs.

  16. Genetic Algorithms Applied to Multi-Objective Aerodynamic Shape Optimization

    Science.gov (United States)

    Holst, Terry L.

    2005-01-01

    A genetic algorithm approach suitable for solving multi-objective problems is described and evaluated using a series of aerodynamic shape optimization problems. Several new features including two variations of a binning selection algorithm and a gene-space transformation procedure are included. The genetic algorithm is suitable for finding Pareto optimal solutions in search spaces that are defined by any number of genes and that contain any number of local extrema. A new masking array capability is included allowing any gene or gene subset to be eliminated as decision variables from the design space. This allows determination of the effect of a single gene or gene subset on the Pareto optimal solution. Results indicate that the genetic algorithm optimization approach is flexible in application and reliable. The binning selection algorithms generally provide Pareto front quality enhancements and moderate convergence efficiency improvements for most of the problems solved.

  17. Application of genetic algorithms to hydrogenated silicon clusters

    Indian Academy of Sciences (India)

    Hydrogenated silicon; genetic algorithms; differential evolution; ab initio calculation. ... with the earlier work conducted using the simulated annealing technique. ... Department of Metallurgical and Materials Engineering, Indian Institute of ...

  18. Robust reactor power control system design by genetic algorithm

    Energy Technology Data Exchange (ETDEWEB)

    Lee, Yoon Joon; Cho, Kyung Ho; Kim, Sin [Cheju National University, Cheju (Korea, Republic of)

    1997-12-31

    The H{sub {infinity}} robust controller for the reactor power control system is designed by use of the mixed weight sensitivity. The system is configured into the typical two-port model with which the weight functions are augmented. Since the solution depends on the weighting functions and the problem is of nonconvex, the genetic algorithm is used to determine the weighting functions. The cost function applied in the genetic algorithm permits the direct control of the power tracking performances. In addition, the actual operating constraints such as rod velocity and acceleration can be treated as design parameters. Compared with the conventional approach, the controller designed by the genetic algorithm results in the better performances with the realistic constraints. Also, it is found that the genetic algorithm could be used as an effective tool in the robust design. 4 refs., 6 figs. (Author)

  19. Automated phylogenetic detection of recombination using a genetic algorithm

    National Research Council Canada - National Science Library

    Kosakovsky Pond, Sergei L; Posada, David; Gravenor, Michael B; Woelk, Christopher H; Frost, Simon D W

    2006-01-01

    .... We propose a model-based framework that uses a genetic algorithm to search a multiple-sequence alignment for putative recombination break points, quantifies the level of support for their locations...

  20. Optimization of genomic selection training populations with a genetic algorithm

    National Research Council Canada - National Science Library

    Akdemir, Deniz; Sanchez, Julio I; Jannink, Jean-Luc

    2015-01-01

    ...) for a given set of individuals (test set) based on a training set of individuals. We used this reliability measure with a genetic algorithm scheme to find an optimized training set from a larger set of candidate individuals...

  1. Fuel Cell Impedance Model Parameters Optimization using a Genetic Algorithm

    National Research Council Canada - National Science Library

    Mohamed Selmene Ben Yahia; Hatem Allagui; Arafet Bouaicha; Abdelkader Mami

    2017-01-01

    .... The method used for the identification is a sample genetic algorithm and the proposed impedance model is based on electric parameters, which will be found from a sweeping of well determined frequency bands...

  2. Genetic Algorithm Optimisation of PID Controllers for a Multivariable Process

    OpenAIRE

    Wael Alharbi; Barry Gomm

    2017-01-01

    This project is about the design of PID controllers and the improvement of outputs in multivariable processes. The optimisation of PID controller for the Shell oil process is presented in this paper, using Genetic Algorithms (GAs). Genetic Algorithms (GAs) are used to automatically tune PID controllers according to given specifications. They use an objective function, which is specially formulated and measures the performance of controller in terms of time-domain bounds on the responses of cl...

  3. Superscattering of light optimized by a genetic algorithm

    Energy Technology Data Exchange (ETDEWEB)

    Mirzaei, Ali, E-mail: ali.mirzaei@anu.edu.au; Miroshnichenko, Andrey E.; Shadrivov, Ilya V.; Kivshar, Yuri S. [Nonlinear Physics Center, Research School of Physics and Engineering, Australian National University, Canberra ACT 0200 (Australia)

    2014-07-07

    We analyse scattering of light from multi-layer plasmonic nanowires and employ a genetic algorithm for optimizing the scattering cross section. We apply the mode-expansion method using experimental data for material parameters to demonstrate that our genetic algorithm allows designing realistic core-shell nanostructures with the superscattering effect achieved at any desired wavelength. This approach can be employed for optimizing both superscattering and cloaking at different wavelengths in the visible spectral range.

  4. A novel genetic algorithm for k-LCS

    Science.gov (United States)

    Zheng, Li; Yang, Guoyu; Zhang, Rui

    2017-08-01

    A new fitness function model is designed, which considers that the outstanding sequence must be a common subsequence and the longer the better. However, the sequence which is not a common subsequence should be eliminated. Then, a novel genetic algorithm is proposed and described in details. Finally, the experimental results show that the new fitness function associated with the novel genetic algorithm can find out better solution.

  5. Hybrid genetic algorithm for minimizing non productive machining ...

    African Journals Online (AJOL)

    user

    They include the survival of the fittest idea into a search algorithm, which provides a method of searching which does not need to explore every possible solution in the feasible region to obtain a good result. Genetic algorithms are based on the natural process of evolution. In nature, the fittest individuals are most likely to.

  6. Multi-objective Uniform-diversity Genetic Algorithm (MUGA)

    OpenAIRE

    Jamali, Ali; Nariman-zadeh, Nader; Atashkari, Kazem

    2008-01-01

    A new multi-objective uniform-diversity genetic algorithm (MUGA) has been proposed and successfully used for some test functions and for thermodynamic cycle optimization of ideal turbojet engines. It has been shown that the performance of this algorithm is superior to that

  7. Navigation Constellation Design Using a Multi-Objective Genetic Algorithm

    Science.gov (United States)

    2015-03-26

    to validate their success. The FUEGO constellation is a constellation of small satellites that detect forest fires from LEO. The first algorithm... Urbana -Champaign, 2001. 26. N. Srinivas and K. Deb, "Multi-Objective Optimization Using Non-Dominating Sorting in Genetic Algorithms," Evolutionary

  8. Disaggregated Imaging Spacecraft Constellation Optimization with a Genetic Algorithm

    Science.gov (United States)

    2014-03-27

    algorithm used throughout. Genetic algorithms are meant to simulate biological evolution and Charles Darwin’s theory of natural selection [41, 42]. Just like...Advances in artificial life: Darwin meets von Neumann - Volume Part II , ECAL’09, Springer-Verlag, Berlin, Heidelberg, 2011, pp. 334–341, Ac- cessed

  9. GENETIC ALGORITHM FOR BEAM MATCHING IN PATH MANAGER

    CERN Document Server

    Coco, V

    2004-01-01

    In order to compute matching of beam in Path Manager, a genetic algorithm tool has been implemented in this software. This note explains the interest of using numerical optimisation method for optimising beam line. The operation mode of this algorithm, the way it has been used for this specific case and what have been added in Path Manager, will be described.

  10. Research on laser marking speed optimization by using genetic algorithm.

    Science.gov (United States)

    Wang, Dongyun; Yu, Qiwei; Zhang, Yu

    2015-01-01

    Laser Marking Machine is the most common coding equipment on product packaging lines. However, the speed of laser marking has become a bottleneck of production. In order to remove this bottleneck, a new method based on a genetic algorithm is designed. On the basis of this algorithm, a controller was designed and simulations and experiments were performed. The results show that using this algorithm could effectively improve laser marking efficiency by 25%.

  11. Dynamic Uniform Scaling for Multiobjective Genetic Algorithms

    DEFF Research Database (Denmark)

    Pedersen, Gerulf; Goldberg, David E.

    2004-01-01

    Before Multiobjective Evolutionary Algorithms (MOEAs) can be used as a widespread tool for solving arbitrary real world problems there are some salient issues which require further investigation. One of these issues is how a uniform distribution of solutions along the Pareto non-dominated front can......, the issue of obtaining a diverse set of solutions for badly scaled objective functions will be investigated and proposed solutions will be implemented using the NSGA-II algorithm....

  12. Heuristic Algorithms for Solving Bounded Diameter Minimum Spanning Tree Problem and Its Application to Genetic Algorithm Development

    OpenAIRE

    Nghia, Nguyen Duc; Binh, Huynh Thi Thanh

    2008-01-01

    We have introduced the heuristic algorithm for solving BDMST problem, called CBRC. The experiment shows that CBRC have best result than the other known heuristic algorithm for solving BDMST prolem on Euclidean instances. The best solution found by the genetic algorithm which uses best heuristic algorithm or only one heuristic algorithm for initialization the population is not better than the best solution found by the genetic algorithm which uses mixed heuristic algorithms (randomized heurist...

  13. Multi-objective genetic algorithm for solving N-version program design problem

    Energy Technology Data Exchange (ETDEWEB)

    Yamachi, Hidemi [Department of Computer and Information Engineering, Nippon Institute of Technology, Miyashiro, Saitama 345-8501 (Japan) and Department of Production and Information Systems Engineering, Tokyo Metropolitan Institute of Technology, Hino, Tokyo 191-0065 (Japan)]. E-mail: yamachi@nit.ac.jp; Tsujimura, Yasuhiro [Department of Computer and Information Engineering, Nippon Institute of Technology, Miyashiro, Saitama 345-8501 (Japan)]. E-mail: tujimr@nit.ac.jp; Kambayashi, Yasushi [Department of Computer and Information Engineering, Nippon Institute of Technology, Miyashiro, Saitama 345-8501 (Japan)]. E-mail: yasushi@nit.ac.jp; Yamamoto, Hisashi [Department of Production and Information Systems Engineering, Tokyo Metropolitan Institute of Technology, Hino, Tokyo 191-0065 (Japan)]. E-mail: yamamoto@cc.tmit.ac.jp

    2006-09-15

    N-version programming (NVP) is a programming approach for constructing fault tolerant software systems. Generally, an optimization model utilized in NVP selects the optimal set of versions for each module to maximize the system reliability and to constrain the total cost to remain within a given budget. In such a model, while the number of versions included in the obtained solution is generally reduced, the budget restriction may be so rigid that it may fail to find the optimal solution. In order to ameliorate this problem, this paper proposes a novel bi-objective optimization model that maximizes the system reliability and minimizes the system total cost for designing N-version software systems. When solving multi-objective optimization problem, it is crucial to find Pareto solutions. It is, however, not easy to obtain them. In this paper, we propose a novel bi-objective optimization model that obtains many Pareto solutions efficiently. We formulate the optimal design problem of NVP as a bi-objective 0-1 nonlinear integer programming problem. In order to overcome this problem, we propose a Multi-objective genetic algorithm (MOGA), which is a powerful, though time-consuming, method to solve multi-objective optimization problems. When implementing genetic algorithm (GA), the use of an appropriate genetic representation scheme is one of the most important issues to obtain good performance. We employ random-key representation in our MOGA to find many Pareto solutions spaced as evenly as possible along the Pareto frontier. To pursue improve further performance, we introduce elitism, the Pareto-insertion and the Pareto-deletion operations based on distance between Pareto solutions in the selection process. The proposed MOGA obtains many Pareto solutions along the Pareto frontier evenly. The user of the MOGA can select the best compromise solution among the candidates by controlling the balance between the system reliability and the total cost.

  14. Optimization of genomic selection training populations with a genetic algorithm

    Science.gov (United States)

    In this article, we derive a computationally efficient statistic to measure the reliability of estimates of genetic breeding values for a fixed set of genotypes based on a given training set of genotypes and phenotypes. We adopt a genetic algorithm scheme to find a training set of certain size from ...

  15. Evolving aerodynamic airfoils for wind turbines through a genetic algorithm

    Science.gov (United States)

    Hernández, J. J.; Gómez, E.; Grageda, J. I.; Couder, C.; Solís, A.; Hanotel, C. L.; Ledesma, JI

    2017-01-01

    Nowadays, genetic algorithms stand out for airfoil optimisation, due to the virtues of mutation and crossing-over techniques. In this work we propose a genetic algorithm with arithmetic crossover rules. The optimisation criteria are taken to be the maximisation of both aerodynamic efficiency and lift coefficient, while minimising drag coefficient. Such algorithm shows greatly improvements in computational costs, as well as a high performance by obtaining optimised airfoils for Mexico City's specific wind conditions from generic wind turbines designed for higher Reynolds numbers, in few iterations.

  16. A Modified Decision Tree Algorithm Based on Genetic Algorithm for Mobile User Classification Problem

    Science.gov (United States)

    Liu, Dong-sheng; Fan, Shu-jiang

    2014-01-01

    In order to offer mobile customers better service, we should classify the mobile user firstly. Aimed at the limitations of previous classification methods, this paper puts forward a modified decision tree algorithm for mobile user classification, which introduced genetic algorithm to optimize the results of the decision tree algorithm. We also take the context information as a classification attributes for the mobile user and we classify the context into public context and private context classes. Then we analyze the processes and operators of the algorithm. At last, we make an experiment on the mobile user with the algorithm, we can classify the mobile user into Basic service user, E-service user, Plus service user, and Total service user classes and we can also get some rules about the mobile user. Compared to C4.5 decision tree algorithm and SVM algorithm, the algorithm we proposed in this paper has higher accuracy and more simplicity. PMID:24688389

  17. Application of Genetic Algorithm for Solving Optimum Power Flow Problems

    OpenAIRE

    Vennila, H.; T. Ruban Deva Prakash; B.G. Malini; M.S. Birundha; V. Evangelin Jeba; L. Sumi

    2013-01-01

    An efficient and optimum economic operation and planning of electric power generation systems is very important. The basic requirement of power economic dispatch (ED) is to generate adequate electricity to meet load demand at the lowest possible cost under a number of constrains. Genetic Algorithms (GA) represents a class of general purpose stochastic search techniques which simulate natural inheritance by genetics. In this paper, the principles of genetics involving natural selection and evo...

  18. Application of Genetic Algorithms for Driverless Subway Train Energy Optimization

    Directory of Open Access Journals (Sweden)

    Morris Brenna

    2016-01-01

    Full Text Available After an introduction on the basic aspects of electric railway transports, focusing mainly on driverless subways and their related automation systems (ATC, ATP, and ATO, a technique for energy optimization of the train movement through their control using genetic algorithms will be presented. Genetic algorithms are a heuristic search and iterative stochastic method used in computing to find exact or approximate solutions to optimization problems. This optimization process has been calculated and tested on a real subway line in Milan through the implementation of a dedicated Matlab code. The so-defined algorithm provides the optimization of the trains movement through a coast control table created by the use of a genetic algorithm that minimizes the energy consumption and the train scheduled time. The obtained results suggest that the method is promising in minimizing the energy consumption of the electric trains.

  19. Dynamic Uniform Scaling for Multiobjective Genetic Algorithms

    DEFF Research Database (Denmark)

    Pedersen, Gerulf; Goldberg, D.E.

    2004-01-01

    Before Multiobjective Evolutionary Algorithms (MOEAs) can be used as a widespread tool for solving arbitrary real world problems there are some salient issues which require further investigation. One of these issues is how a uniform distribution of solutions along the Pareto non-dominated front can...... be obtained for badly scaled objective functions. This is especially a problems if the bounds for the objective functions are unknown, which may result in the non-dominated solutions found by the MOEA to be biased towards one objective, this resulting in a less diverse set of tradeoffs. In this paper......, the issue of obtaining a diverse set of solutions for badly scaled objective functions will be investigated and proposed solutions will be implemented using the NSGA-II algorithm....

  20. G/SPLINES: A hybrid of Friedman's Multivariate Adaptive Regression Splines (MARS) algorithm with Holland's genetic algorithm

    Science.gov (United States)

    Rogers, David

    1991-01-01

    G/SPLINES are a hybrid of Friedman's Multivariable Adaptive Regression Splines (MARS) algorithm with Holland's Genetic Algorithm. In this hybrid, the incremental search is replaced by a genetic search. The G/SPLINE algorithm exhibits performance comparable to that of the MARS algorithm, requires fewer least squares computations, and allows significantly larger problems to be considered.

  1. An improved genetic algorithm for searching for pollution sources

    Directory of Open Access Journals (Sweden)

    Quan-min BU

    2013-10-01

    Full Text Available As an optimization method that has experienced rapid development over the past 20 years, the genetic algorithm has been successfully applied in many fields, but it requires repeated searches based on the characteristics of high-speed computer calculation and conditions of the known relationship between the objective function and independent variables. There are several hundred generations of evolvement, but the functional relationship is unknown in pollution source searches. Therefore, the genetic algorithm cannot be used directly. Certain improvements need to be made based on the actual situation, so that the genetic algorithm can adapt to the actual conditions of environmental problems, and can be used in environmental monitoring and environmental quality assessment. Therefore, a series of methods are proposed for the improvement of the genetic algorithm: (1 the initial generation of individual groups should be artificially set and move from lightly polluted areas to heavily polluted areas; (2 intervention measures should be introduced in the competition between individuals; (3 guide individuals should be added; and (4 specific improvement programs should be put forward. Finally, the scientific rigor and rationality of the improved genetic algorithm are proven through an example.

  2. A parallel genetic algorithm for the set partitioning problem

    Energy Technology Data Exchange (ETDEWEB)

    Levine, D. [Argonne National Lab., IL (United States). Mathematics and Computer Science Division.

    1994-05-01

    In this dissertation the author reports on his efforts to develop a parallel genetic algorithm and apply it to the solution of set partitioning problem -- a difficult combinatorial optimization problem used by many airlines as a mathematical model for flight crew scheduling. He developed a distributed steady-state genetic algorithm in conjunction with a specialized local search heuristic for solving the set partitioning problem. The genetic algorithm is based on an island model where multiple independent subpopulations each run a steady-state genetic algorithm on their subpopulation and occasionally fit strings migrate between the subpopulations. Tests on forty real-world set partitioning problems were carried out on up to 128 nodes of an IBM SP1 parallel computer. The authors found that performance, as measured by the quality of the solution found and the iteration on which it was found, improved as additional subpopulation found and the iteration on which it was found, improved as additional subpopulations were added to the computation. With larger numbers of subpopulations the genetic algorithm was regularly able to find the optimal solution to problems having up to a few thousand integer variables. In two cases, high-quality integer feasible solutions were found for problems with 36,699 and 43,749 integer variables, respectively. A notable limitation they found was the difficulty solving problems with many constraints.

  3. Quantum algorithms and the genetic code

    Indian Academy of Sciences (India)

    Home; Journals; Pramana – Journal of Physics; Volume 56; Issue 2-3. Quantum ... Quantum information processing Volume 56 Issue 2-3 February-March 2001 pp 367-381 ... Quantum mechanics; computation; database search; genetic information; DNA; nucleotide base; protein; amino acid; enzyme; quantum coherence.

  4. Pose estimation for augmented reality applications using genetic algorithm.

    Science.gov (United States)

    Yu, Ying Kin; Wong, Kin Hong; Chang, Michael Ming Yuen

    2005-12-01

    This paper describes a genetic algorithm that tackles the pose-estimation problem in computer vision. Our genetic algorithm can find the rotation and translation of an object accurately when the three-dimensional structure of the object is given. In our implementation, each chromosome encodes both the pose and the indexes to the selected point features of the object. Instead of only searching for the pose as in the existing work, our algorithm, at the same time, searches for a set containing the most reliable feature points in the process. This mismatch filtering strategy successfully makes the algorithm more robust under the presence of point mismatches and outliers in the images. Our algorithm has been tested with both synthetic and real data with good results. The accuracy of the recovered pose is compared to the existing algorithms. Our approach outperformed the Lowe's method and the other two genetic algorithms under the presence of point mismatches and outliers. In addition, it has been used to estimate the pose of a real object. It is shown that the proposed method is applicable to augmented reality applications.

  5. Bayesian network structure learning using chaos hybrid genetic algorithm

    Science.gov (United States)

    Shen, Jiajie; Lin, Feng; Sun, Wei; Chang, KC

    2012-06-01

    A new Bayesian network (BN) learning method using a hybrid algorithm and chaos theory is proposed. The principles of mutation and crossover in genetic algorithm and the cloud-based adaptive inertia weight were incorporated into the proposed simple particle swarm optimization (sPSO) algorithm to achieve better diversity, and improve the convergence speed. By means of ergodicity and randomicity of chaos algorithm, the initial network structure population is generated by using chaotic mapping with uniform search under structure constraints. When the algorithm converges to a local minimal, a chaotic searching is started to skip the local minima and to identify a potentially better network structure. The experiment results show that this algorithm can be effectively used for BN structure learning.

  6. Parametric analysis for genetic algorithms handling parameters

    Directory of Open Access Journals (Sweden)

    A.R. Ayad

    2013-03-01

    Full Text Available In the present paper, Evolutionary Algorithms (EAs computing techniques have been used for economical studies that concern water distribution networks, such as, economical design of pipe network, parallel expansion, and pipe rehabilitation and maintenance. EAs are used because of capability of searching vast and complex search space and locating near global optimal solutions rapidly. A model created under the name “EAnet” combines GA models with ELGTnet as hydraulic analysis models to obtain optimal design of water pipe networks. Finally, summary of key findings and recommended parameters to be used is presented.

  7. Genetic algorithms with permutation coding for multiple sequence alignment.

    Science.gov (United States)

    Ben Othman, Mohamed Tahar; Abdel-Azim, Gamil

    2013-08-01

    Multiple sequence alignment (MSA) is one of the topics of bio informatics that has seriously been researched. It is known as NP-complete problem. It is also considered as one of the most important and daunting tasks in computational biology. Concerning this a wide number of heuristic algorithms have been proposed to find optimal alignment. Among these heuristic algorithms are genetic algorithms (GA). The GA has mainly two major weaknesses: it is time consuming and can cause local minima. One of the significant aspects in the GA process in MSA is to maximize the similarities between sequences by adding and shuffling the gaps of Solution Coding (SC). Several ways for SC have been introduced. One of them is the Permutation Coding (PC). We propose a hybrid algorithm based on genetic algorithms (GAs) with a PC and 2-opt algorithm. The PC helps to code the MSA solution which maximizes the gain of resources, reliability and diversity of GA. The use of the PC opens the area by applying all functions over permutations for MSA. Thus, we suggest an algorithm to calculate the scoring function for multiple alignments based on PC, which is used as fitness function. The time complexity of the GA is reduced by using this algorithm. Our GA is implemented with different selections strategies and different crossovers. The probability of crossover and mutation is set as one strategy. Relevant patents have been probed in the topic.

  8. Distributed genetic algorithms for the floorplan design problem

    Science.gov (United States)

    Cohoon, James P.; Hegde, Shailesh U.; Martin, Worthy N.; Richards, Dana S.

    1991-01-01

    Designing a VLSI floorplan calls for arranging a given set of modules in the plane to minimize the weighted sum of area and wire-length measures. A method of solving the floorplan design problem using distributed genetic algorithms is presented. Distributed genetic algorithms, based on the paleontological theory of punctuated equilibria, offer a conceptual modification to the traditional genetic algorithms. Experimental results on several problem instances demonstrate the efficacy of this method and indicate the advantages of this method over other methods, such as simulated annealing. The method has performed better than the simulated annealing approach, both in terms of the average cost of the solutions found and the best-found solution, in almost all the problem instances tried.

  9. A genetic algorithm approach in interface and surface structure optimization

    Energy Technology Data Exchange (ETDEWEB)

    Zhang, Jian [Iowa State Univ., Ames, IA (United States)

    2010-01-01

    The thesis is divided into two parts. In the first part a global optimization method is developed for the interface and surface structures optimization. Two prototype systems are chosen to be studied. One is Si[001] symmetric tilted grain boundaries and the other is Ag/Au induced Si(111) surface. It is found that Genetic Algorithm is very efficient in finding lowest energy structures in both cases. Not only existing structures in the experiments can be reproduced, but also many new structures can be predicted using Genetic Algorithm. Thus it is shown that Genetic Algorithm is a extremely powerful tool for the material structures predictions. The second part of the thesis is devoted to the explanation of an experimental observation of thermal radiation from three-dimensional tungsten photonic crystal structures. The experimental results seems astounding and confusing, yet the theoretical models in the paper revealed the physics insight behind the phenomena and can well reproduced the experimental results.

  10. OPTIMAL-TUNING OF PID CONTROLLER GAINS USING GENETIC ALGORITHMS

    Directory of Open Access Journals (Sweden)

    Ömer GÜNDOĞDU

    2005-01-01

    Full Text Available This paper presents a method of optimum parameter tuning of a PID controller to be used in driving an inertial load by a dc motor thorough a gearbox. Specifically, the method uses genetic algorithms to determine the optimum controller parameters by minimizing the sum of the integral of the squared error and the squared controller output deviated from its steady state value. The paper suggests the use of Ziegler-Nichols settings to form the intervals for the controller parameters in which the population to be formed. The results obtained from the genetic algorithms are compared with the ones from Ziegler-Nichols in both figures and tabular form. Comparatively better results are obtained in the genetic algorithm case.

  11. A Wideband Direct Data Domain Genetic Algorithm Beamforming

    Directory of Open Access Journals (Sweden)

    H. M. Elkamchouchi

    2015-04-01

    Full Text Available In this paper, a wideband direct data-domain genetic algorithm beamforming is presented. Received wideband signals are decomposed to a set of narrow sub-bands using fast Fourier transform. Each sub-band is transformed to a reference frequency using the steering vector transformation. So, narrowband approaches could be used for any of these sub-bands. Hence, the direct data-domain genetic algorithm beamforming can be used to form a single ‘hybrid’ beam pattern with sufficiently deep nulls in order to separate and reconstruct frequency components of the signal of interest efficiently. The proposed approach avoids most of drawbacks of already-existing statistical and gradient-based approaches since formation of a covariance matrix is not needed, and a genetic algorithm is used to solve the beamforming problem.

  12. Big Data Clustering Using Genetic Algorithm On Hadoop Mapreduce

    Directory of Open Access Journals (Sweden)

    Nivranshu Hans

    2015-04-01

    Full Text Available Abstract Cluster analysis is used to classify similar objects under same group. It is one of the most important data mining methods. However it fails to perform well for big data due to huge time complexity. For such scenarios parallelization is a better approach. Mapreduce is a popular programming model which enables parallel processing in a distributed environment. But most of the clustering algorithms are not naturally parallelizable for instance Genetic Algorithms. This is so due to the sequential nature of Genetic Algorithms. This paper introduces a technique to parallelize GA based clustering by extending hadoop mapreduce. An analysis of proposed approach to evaluate performance gains with respect to a sequential algorithm is presented. The analysis is based on a real life large data set.

  13. Combinatorial optimization problem solution based on improved genetic algorithm

    Science.gov (United States)

    Zhang, Peng

    2017-08-01

    Traveling salesman problem (TSP) is a classic combinatorial optimization problem. It is a simplified form of many complex problems. In the process of study and research, it is understood that the parameters that affect the performance of genetic algorithm mainly include the quality of initial population, the population size, and crossover probability and mutation probability values. As a result, an improved genetic algorithm for solving TSP problems is put forward. The population is graded according to individual similarity, and different operations are performed to different levels of individuals. In addition, elitist retention strategy is adopted at each level, and the crossover operator and mutation operator are improved. Several experiments are designed to verify the feasibility of the algorithm. Through the experimental results analysis, it is proved that the improved algorithm can improve the accuracy and efficiency of the solution.

  14. Genetic Algorithms and Genetic Programming Modern Concepts and Practical Applications

    CERN Document Server

    Affenzeller, Michael

    2009-01-01

    Describes several generic algorithmic concepts that can be used in various kinds of GA or with evolutionary optimization techniques. This title provides a better understanding of the basic workflow of GAs and GP, encouraging readers to establish new bionic, problem-independent theoretical concepts.

  15. Genetic algorithm for multi-objective experimental optimization

    OpenAIRE

    Link, Hannes; Weuster-Botz, Dirk

    2006-01-01

    A new software tool making use of a genetic algorithm for multi-objective experimental optimization (GAME.opt) was developed based on a strength Pareto evolutionary algorithm. The software deals with high dimensional variable spaces and unknown interactions of design variables. This approach was evaluated by means of multi-objective test problems replacing the experimental results. A default parameter setting is proposed enabling users without expert knowledge to minimize the experimental eff...

  16. GAMBL, genetic algorithm optimization of memory-based WSD

    OpenAIRE

    Decadt, Bart; Hoste, Veronique; Daelemans, Walter; van den Bosch, Antal

    2004-01-01

    GAMBL is a word expert approach to WSD in which each word expert is trained using memory based learning. Joint feature selection and algorithm parameter optimization are achieved with a genetic algorithm (GA). We use a cascaded classifier approach in which the GA optimizes local context features and the output of a separate keyword classifier (rather than also optimizing the keyword features together with the local context features). A further innovation on earlier versions of memory based WS...

  17. Genetic algorithm for multi-objective experimental optimization

    Science.gov (United States)

    Link, Hannes

    2006-01-01

    A new software tool making use of a genetic algorithm for multi-objective experimental optimization (GAME.opt) was developed based on a strength Pareto evolutionary algorithm. The software deals with high dimensional variable spaces and unknown interactions of design variables. This approach was evaluated by means of multi-objective test problems replacing the experimental results. A default parameter setting is proposed enabling users without expert knowledge to minimize the experimental effort (small population sizes and few generations). PMID:17048033

  18. Automatic page layout using genetic algorithms for electronic albuming

    Science.gov (United States)

    Geigel, Joe; Loui, Alexander C. P.

    2000-12-01

    In this paper, we describe a flexible system for automatic page layout that makes use of genetic algorithms for albuming applications. The system is divided into two modules, a page creator module which is responsible for distributing images amongst various album pages, and an image placement module which positions images on individual pages. Final page layouts are specified in a textual form using XML for printing or viewing over the Internet. The system makes use of genetic algorithms, a class of search and optimization algorithms that are based on the concepts of biological evolution, for generating solutions with fitness based on graphic design preferences supplied by the user. The genetic page layout algorithm has been incorporated into a web-based prototype system for interactive page layout over the Internet. The prototype system is built using client-server architecture and is implemented in java. The system described in this paper has demonstrated the feasibility of using genetic algorithms for automated page layout in albuming and web-based imaging applications. We believe that the system adequately proves the validity of the concept, providing creative layouts in a reasonable number of iterations. By optimizing the layout parameters of the fitness function, we hope to further improve the quality of the final layout in terms of user preference and computation speed.

  19. Naturally selecting solutions: the use of genetic algorithms in bioinformatics.

    Science.gov (United States)

    Manning, Timmy; Sleator, Roy D; Walsh, Paul

    2013-01-01

    For decades, computer scientists have looked to nature for biologically inspired solutions to computational problems; ranging from robotic control to scheduling optimization. Paradoxically, as we move deeper into the post-genomics era, the reverse is occurring, as biologists and bioinformaticians look to computational techniques, to solve a variety of biological problems. One of the most common biologically inspired techniques are genetic algorithms (GAs), which take the Darwinian concept of natural selection as the driving force behind systems for solving real world problems, including those in the bioinformatics domain. Herein, we provide an overview of genetic algorithms and survey some of the most recent applications of this approach to bioinformatics based problems.

  20. Constraint genetic algorithm and its application in sintering proportioning

    Science.gov (United States)

    Wu, Tiebin; Liu, Yunlian; Tang, Wenyan; Li, Xinjun; Yu, Yi

    2017-09-01

    This paper puts forward a method for constrained optimization problems based on self-adaptive penalty function and improved genetic algorithm. In order to improve the speed of convergence and avoid premature convergence, a method based on good-point set theory has been proposed. By using good point set method for generating initial population, the initial population is uniformly distributed in the solution space. This paper Designs an elite reverse learning strategy, and proposes a mechanism to automatically adjust the crossover probability according to the individual advantages and disadvantages. The tests indicate that the proposed constrained genetic algorithm is efficient and feasible.

  1. Application of genetic algorithm in radio ecological models parameter determination

    Energy Technology Data Exchange (ETDEWEB)

    Pantelic, G. [Institute of Occupatioanl Health and Radiological Protection ' Dr Dragomir Karajovic' , Belgrade (Serbia)

    2006-07-01

    The method of genetic algorithms was used to determine the biological half-life of 137 Cs in cow milk after the accident in Chernobyl. Methodologically genetic algorithms are based on the fact that natural processes tend to optimize themselves and therefore this method should be more efficient in providing optimal solutions in the modeling of radio ecological and environmental events. The calculated biological half-life of 137 Cs in milk is (32 {+-} 3) days and transfer coefficient from grass to milk is (0.019 {+-} 0.005). (authors)

  2. Particle swarm optimization - Genetic algorithm (PSOGA) on linear transportation problem

    Science.gov (United States)

    Rahmalia, Dinita

    2017-08-01

    Linear Transportation Problem (LTP) is the case of constrained optimization where we want to minimize cost subject to the balance of the number of supply and the number of demand. The exact method such as northwest corner, vogel, russel, minimal cost have been applied at approaching optimal solution. In this paper, we use heurisitic like Particle Swarm Optimization (PSO) for solving linear transportation problem at any size of decision variable. In addition, we combine mutation operator of Genetic Algorithm (GA) at PSO to improve optimal solution. This method is called Particle Swarm Optimization - Genetic Algorithm (PSOGA). The simulations show that PSOGA can improve optimal solution resulted by PSO.

  3. Real coded genetic algorithm for fuzzy time series prediction

    Science.gov (United States)

    Jain, Shilpa; Bisht, Dinesh C. S.; Singh, Phool; Mathpal, Prakash C.

    2017-10-01

    Genetic Algorithm (GA) forms a subset of evolutionary computing, rapidly growing area of Artificial Intelligence (A.I.). Some variants of GA are binary GA, real GA, messy GA, micro GA, saw tooth GA, differential evolution GA. This research article presents a real coded GA for predicting enrollments of University of Alabama. Data of Alabama University is a fuzzy time series. Here, fuzzy logic is used to predict enrollments of Alabama University and genetic algorithm optimizes fuzzy intervals. Results are compared to other eminent author works and found satisfactory, and states that real coded GA are fast and accurate.

  4. A Genetic Algorithm for the Single Machine Maximum Lateness Problem

    OpenAIRE

    V. SELS; M. VANHOUCKE

    2009-01-01

    We consider the problem of scheduling a number of jobs, each job having a release time, a processing time and a due date, on a single machine with the objective of minimizing the maximum lateness or tardiness. This problem often occurs as a sub-problem in solving other scheduling environments such as flow shops or job shops. We developed a genetic algorithm and compared its performance with alternative methods on diverse data sets. Based on a literature study on genetic algorithms in single m...

  5. Acoustic design of rotor blades using a genetic algorithm

    Science.gov (United States)

    Wells, V. L.; Han, A. Y.; Crossley, W. A.

    1995-01-01

    A genetic algorithm coupled with a simplified acoustic analysis was used to generate low-noise rotor blade designs. The model includes thickness, steady loading and blade-vortex interaction noise estimates. The paper presents solutions for several variations in the fitness function, including thickness noise only, loading noise only, and combinations of the noise types. Preliminary results indicate that the analysis provides reasonable assessments of the noise produced, and that genetic algorithm successfully searches for 'good' designs. The results show that, for a given required thrust coefficient, proper blade design can noticeably reduce the noise produced at some expense to the power requirements.

  6. Multi Population Hybrid Genetic Algorithms for University Course Timetabling

    Directory of Open Access Journals (Sweden)

    Mehrnaz Shirani LIRI

    2012-08-01

    Full Text Available University course timetabling is one of the important and time consuming issues that each University is involved with at the beginning of each university year. This problem is in class of NP-hard problem and is very difficult to solve by classic algorithms. Therefore optimization techniques are used to solve them and produce optimal or almost optimal feasible solutions instead of exact solutions. Genetic algorithms, because of their multidirectional search property, are considered as an efficient approach for solving this type of problems. In this paper three new hybrid genetic algorithms for solving the university course timetabling problem (UCTP are proposed: FGARI, FGASA and FGATS. In the proposed algorithms, fuzzy logic is used to measure violation of soft constraints in fitness function to deal with inherent uncertainty and vagueness involved in real life data. Also, randomized iterative local search, simulated annealing and tabu search are applied, respectively, to improve exploitive search ability and prevent genetic algorithm to be trapped in local optimum. The experimental results indicate that the proposed algorithms are able to produce promising results for the UCTP

  7. Neural-Network-Biased Genetic Algorithms for Materials Design: Evolutionary Algorithms That Learn.

    Science.gov (United States)

    Patra, Tarak K; Meenakshisundaram, Venkatesh; Hung, Jui-Hsiang; Simmons, David S

    2017-02-13

    Machine learning has the potential to dramatically accelerate high-throughput approaches to materials design, as demonstrated by successes in biomolecular design and hard materials design. However, in the search for new soft materials exhibiting properties and performance beyond those previously achieved, machine learning approaches are frequently limited by two shortcomings. First, because they are intrinsically interpolative, they are better suited to the optimization of properties within the known range of accessible behavior than to the discovery of new materials with extremal behavior. Second, they require large pre-existing data sets, which are frequently unavailable and prohibitively expensive to produce. Here we describe a new strategy, the neural-network-biased genetic algorithm (NBGA), for combining genetic algorithms, machine learning, and high-throughput computation or experiment to discover materials with extremal properties in the absence of pre-existing data. Within this strategy, predictions from a progressively constructed artificial neural network are employed to bias the evolution of a genetic algorithm, with fitness evaluations performed via direct simulation or experiment. In effect, this strategy gives the evolutionary algorithm the ability to "learn" and draw inferences from its experience to accelerate the evolutionary process. We test this algorithm against several standard optimization problems and polymer design problems and demonstrate that it matches and typically exceeds the efficiency and reproducibility of standard approaches including a direct-evaluation genetic algorithm and a neural-network-evaluated genetic algorithm. The success of this algorithm in a range of test problems indicates that the NBGA provides a robust strategy for employing informatics-accelerated high-throughput methods to accelerate materials design in the absence of pre-existing data.

  8. An Adaptive Filtering Algorithm Based on Genetic Algorithm-Backpropagation Network

    Directory of Open Access Journals (Sweden)

    Kai Hu

    2013-01-01

    Full Text Available A new image filtering algorithm is proposed. GA-BPN algorithm uses genetic algorithm (GA to decide weights in a back propagation neural network (BPN. It has better global optimal characteristics than traditional optimal algorithm. In this paper, we used GA-BPN to do image noise filter researching work. Firstly, this paper uses training samples to train GA-BPN as the noise detector. Then, we utilize the well-trained GA-BPN to recognize noise pixels in target image. And at last, an adaptive weighted average algorithm is used to recover noise pixels recognized by GA-BPN. Experiment data shows that this algorithm has better performance than other filters.

  9. Improved Runtime Analysis of the Simple Genetic Algorithm

    DEFF Research Database (Denmark)

    Oliveto, Pietro S.; Witt, Carsten

    2013-01-01

    A runtime analysis of the Simple Genetic Algorithm (SGA) for the OneMax problem has recently been presented proving that the algorithm requires exponential time with overwhelming probability. This paper presents an improved analysis which overcomes some limitations of our previous one. Firstly...... improvement towards the reusability of the techniques in future systematic analyses of GAs. Finally, we consider the more natural SGA using selection with replacement rather than without replacement although the results hold for both algorithmic versions. Experiments are presented to explore the limits...

  10. Improved time complexity analysis of the Simple Genetic Algorithm

    DEFF Research Database (Denmark)

    Oliveto, Pietro S.; Witt, Carsten

    2015-01-01

    A runtime analysis of the Simple Genetic Algorithm (SGA) for the OneMax problem has recently been presented proving that the algorithm with population size μ≤n1/8−ε requires exponential time with overwhelming probability. This paper presents an improved analysis which overcomes some limitations...... this is a major improvement towards the reusability of the techniques in future systematic analyses of GAs. Finally, we consider the more natural SGA using selection with replacement rather than without replacement although the results hold for both algorithmic versions. Experiments are presented to explore...

  11. Genetic algorithm for chromaticity correction in diffraction limited storage rings

    Directory of Open Access Journals (Sweden)

    M. P. Ehrlichman

    2016-04-01

    Full Text Available A multiobjective genetic algorithm is developed for optimizing nonlinearities in diffraction limited storage rings. This algorithm determines sextupole and octupole strengths for chromaticity correction that deliver optimized dynamic aperture and beam lifetime. The algorithm makes use of dominance constraints to breed desirable properties into the early generations. The momentum aperture is optimized indirectly by constraining the chromatic tune footprint and optimizing the off-energy dynamic aperture. The result is an effective and computationally efficient technique for correcting chromaticity in a storage ring while maintaining optimal dynamic aperture and beam lifetime.

  12. Automated discrete element method calibration using genetic and optimization algorithms

    Science.gov (United States)

    Do, Huy Q.; Aragón, Alejandro M.; Schott, Dingena L.

    2017-06-01

    This research aims at developing a universal methodology for automated calibration of microscopic properties of modelled granular materials. The proposed calibrator can be applied for different experimental set-ups. Two optimization approaches: (1) a genetic algorithm and (2) DIRECT optimization, are used to identify discrete element method input model parameters, e.g., coefficients of sliding and rolling friction. The algorithms are used to minimize the objective function characterized by the discrepancy between the experimental macroscopic properties and the associated numerical results. Two test cases highlight the robustness, stability, and reliability of the two algorithms used for automated discrete element method calibration with different set-ups.

  13. Genetic algorithm for flood detection and evacuation route planning

    Science.gov (United States)

    Gomes, Rahul; Straub, Jeremy

    2017-05-01

    A genetic-type algorithm is presented that uses satellite geospatial data to determine the most probable path to safety for individuals in a disaster area, where a traditional routing system cannot be used. The algorithm uses geological features and disaster information to determine the shortest safe path. It predicts how a flood can change a landform over time and uses this data to predict alternate routes. It also predicts safe routes in rural locations where GPS/map-based routing data is unavailable or inaccurate. Reflectance and a supervised classification algorithm are used and the output is compared with RFPI and PCR-GLOBWB data.

  14. Advancing X-ray scattering metrology using inverse genetic algorithms.

    Science.gov (United States)

    Hannon, Adam F; Sunday, Daniel F; Windover, Donald; Kline, R Joseph

    2016-01-01

    We compare the speed and effectiveness of two genetic optimization algorithms to the results of statistical sampling via a Markov chain Monte Carlo algorithm to find which is the most robust method for determining real space structure in periodic gratings measured using critical dimension small angle X-ray scattering. Both a covariance matrix adaptation evolutionary strategy and differential evolution algorithm are implemented and compared using various objective functions. The algorithms and objective functions are used to minimize differences between diffraction simulations and measured diffraction data. These simulations are parameterized with an electron density model known to roughly correspond to the real space structure of our nanogratings. The study shows that for X-ray scattering data, the covariance matrix adaptation coupled with a mean-absolute error log objective function is the most efficient combination of algorithm and goodness of fit criterion for finding structures with little foreknowledge about the underlying fine scale structure features of the nanograting.

  15. A Genetic Algorithm on Inventory Routing Problem

    Directory of Open Access Journals (Sweden)

    Nevin Aydın

    2014-03-01

    Full Text Available Inventory routing problem can be defined as forming the routes to serve to the retailers from the manufacturer, deciding on the quantity of the shipment to the retailers and deciding on the timing of the replenishments. The difference of inventory routing problems from vehicle routing problems is the consideration of the inventory positions of retailers and supplier, and making the decision accordingly. Inventory routing problems are complex in nature and they can be solved either theoretically or using a heuristics method. Metaheuristics is an emerging class of heuristics that can be applied to combinatorial optimization problems. In this paper, we provide the relationship between vendor-managed inventory and inventory routing problem. The proposed genetic for solving vehicle routing problem is described in detail.

  16. Optimizations of Patch Antenna Arrays Using Genetic Algorithms Supported by the Multilevel Fast Multipole Algorithm

    Directory of Open Access Journals (Sweden)

    C. Onol

    2014-12-01

    Full Text Available We present optimizations of patch antenna arrays using genetic algorithms and highly accurate full-wave solutions of the corresponding radiation problems with the multilevel fast multipole algorithm (MLFMA. Arrays of finite extent are analyzed by using MLFMA, which accounts for all mutual couplings between array elements efficiently and accurately. Using the superposition principle, the number of solutions required for the optimization of an array is reduced to the number of array elements, without resorting to any periodicity and similarity assumptions. Based on numerical experiments, genetic optimizations are improved by considering alternative mutation, crossover, and elitism mechanisms. We show that the developed optimization environment based on genetic algorithms and MLFMA provides efficient and effective optimizations of antenna excitations, which cannot be obtained with array-factor approaches, even for relatively simple arrays with identical elements.

  17. Real-Coded Quantum-Inspired Genetic Algorithm-Based BP Neural Network Algorithm

    Directory of Open Access Journals (Sweden)

    Jianyong Liu

    2015-01-01

    Full Text Available The method that the real-coded quantum-inspired genetic algorithm (RQGA used to optimize the weights and threshold of BP neural network is proposed to overcome the defect that the gradient descent method makes the algorithm easily fall into local optimal value in the learning process. Quantum genetic algorithm (QGA is with good directional global optimization ability, but the conventional QGA is based on binary coding; the speed of calculation is reduced by the coding and decoding processes. So, RQGA is introduced to explore the search space, and the improved varied learning rate is adopted to train the BP neural network. Simulation test shows that the proposed algorithm is effective to rapidly converge to the solution conformed to constraint conditions.

  18. The multi-niche crowding genetic algorithm: Analysis and applications

    Energy Technology Data Exchange (ETDEWEB)

    Cedeno, Walter [Univ. of California, Davis, CA (United States)

    1995-09-01

    The ability of organisms to evolve and adapt to the environment has provided mother nature with a rich and diverse set of species. Only organisms well adapted to their environment can survive from one generation to the next, transferring on the traits, that made them successful, to their offspring. Competition for resources and the ever changing environment drives some species to extinction and at the same time others evolve to maintain the delicate balance in nature. In this disertation we present the multi-niche crowding genetic algorithm, a computational metaphor to the survival of species in ecological niches in the face of competition. The multi-niche crowding genetic algorithm maintains stable subpopulations of solutions in multiple niches in multimodal landscapes. The algorithm introduces the concept of crowding selection to promote mating among members with qirnilar traits while allowing many members of the population to participate in mating. The algorithm uses worst among most similar replacement policy to promote competition among members with similar traits while allowing competition among members of different niches as well. We present empirical and theoretical results for the success of the multiniche crowding genetic algorithm for multimodal function optimization. The properties of the algorithm using different parameters are examined. We test the performance of the algorithm on problems of DNA Mapping, Aquifer Management, and the File Design Problem. Applications that combine the use of heuristics and special operators to solve problems in the areas of combinatorial optimization, grouping, and multi-objective optimization. We conclude by presenting the advantages and disadvantages of the algorithm and describing avenues for future investigation to answer other questions raised by this study.

  19. Influence of crossover methods used by genetic algorithm-based ...

    Indian Academy of Sciences (India)

    order to prevent it and to get into the grammar of exploitation and exploration, parents close to each other are chosen and crossed so that population does ..... Control Syst. Technol. 13(2): 216–223. DeJong K A and Spears W M 1990 An analysis of the interacting roles of population size and crossover in genetic Algorithms.

  20. District Heating Network Design and Configuration Optimization with Genetic Algorithm

    DEFF Research Database (Denmark)

    Li, Hongwei; Svendsen, Svend

    2013-01-01

    and the pipe friction and heat loss formulations are non-linear. In order to find the optimal district heating network configuration, genetic algorithm which handles the mixed integer nonlinear programming problem is chosen. The network configuration is represented with binary and integer encoding...

  1. Applying Genetic Algorithms To Query Optimization in Document Retrieval.

    Science.gov (United States)

    Horng, Jorng-Tzong; Yeh, Ching-Chang

    2000-01-01

    Proposes a novel approach to automatically retrieve keywords and then uses genetic algorithms to adapt the keyword weights. Discusses Chinese text retrieval, term frequency rating formulas, vector space models, bigrams, the PAT-tree structure for information retrieval, query vectors, and relevance feedback. (Author/LRW)

  2. USING GENETIC ALGORITHMS TO DESIGN ENVIRONMENTALLY FRIENDLY PROCESSES

    Science.gov (United States)

    Genetic algorithm calculations are applied to the design of chemical processes to achieve improvements in environmental and economic performance. By finding the set of Pareto (i.e., non-dominated) solutions one can see how different objectives, such as environmental and economic ...

  3. System control fuzzy neural sewage pumping stations using genetic algorithms

    Directory of Open Access Journals (Sweden)

    Владлен Николаевич Кузнецов

    2015-06-01

    Full Text Available It is considered the system of management of sewage pumping station with regulators based on a neuron network with fuzzy logic. Linguistic rules for the controller based on fuzzy logic, maintaining the level of effluent in the receiving tank within the prescribed limits are developed. The use of genetic algorithms for neuron network training is shown.

  4. Concrete Plant Operations Optimization Using Combined Simulation and Genetic Algorithms

    NARCIS (Netherlands)

    Cao, Ming; Lu, Ming; Zhang, Jian-Ping

    2004-01-01

    This work presents a new approach for concrete plant operations optimization by combining a ready mixed concrete (RMC) production simulation tool (called HKCONSIM) with a genetic algorithm (GA) based optimization procedure. A revamped HKCONSIM computer system can be used to automate the simulation

  5. Genetic algorithms in supply chain management: A critical analysis ...

    Indian Academy of Sciences (India)

    Genetic algorithms (GAs) are perhaps the oldest and most frequently used search techniques for dealing with complex and intricate real-life problems that are otherwise difficult to solve by the traditional methods. The present article provides an extensive literature review of the application of GA on supply chain management ...

  6. Identification of partial blockages in pipelines using genetic algorithms

    Indian Academy of Sciences (India)

    A methodology to identify the partial blockages in a simple pipeline using genetic algorithms for non-harmonic flows is presented in this paper. A sinusoidal flow generated by the periodic on-and-off operation of a valve at the outlet is investigated in the time domain and it is observed that pressure variation at the valve is ...

  7. Optimisation of Transfer Function Models using Genetic Algorithms ...

    African Journals Online (AJOL)

    In order to obtain an optimum transfer function estimate, open source software based on genetic algorithm was developed. The software was developed with Visual Basic programming language. In order to test the software, a transfer function model was developed from data obtained from industry. The forecast obtained ...

  8. Use of genetic algorithms for high hydrostatic pressure inactivation ...

    African Journals Online (AJOL)

    Use of genetic algorithms for high hydrostatic pressure inactivation of microorganisms. ... Depending on the properties of HHP equipment (maximum operating pressure) or the type of the food product (heat-sensitive), it could be possible to select the suitable P-T-t trio among the alternatives. This study reveals that GAs could ...

  9. Genetic Algorithm Learning in a New Keynesian Macroeconomic Setup

    NARCIS (Netherlands)

    Hommes, C.; Makarewicz, T.; Massaro, D.; Smits, T.

    2015-01-01

    In order to understand heterogeneous behaviour amongst agents, empirical data from Learning-to-Forecast (LtF) experiments can be used to construct learning models. This paper follows up on Assenza et al. (2013) by using a genetic algorithms (GA) model to replicate the results from their LtF

  10. Genetic Algorithm and its Application in Optimal Sensor Layout

    Directory of Open Access Journals (Sweden)

    Xiang-Yang Chen

    2015-05-01

    Full Text Available This paper aims at the problem of multi sensor station distribution, based on multi- sensor systems of different types as the research object, in the analysis of various types of sensors with different application background, different indicators of demand, based on the different constraints, for all kinds of multi sensor station is studied, the application of genetic algorithms as a tool for the objective function of the models optimization, then the optimal various types of multi sensor station distribution plan, improve the performance of the system, and achieved good military effect. In the field of application of sensor radar, track measuring instrument, the satellite, passive positioning equipment of various types, specific problem, use care indicators and station arrangement between the mathematical model of geometry, using genetic algorithm to get the optimization results station distribution, to solve a variety of practical problems provides useful help, but also reflects the improved genetic algorithm in electronic weapon system based on multi sensor station distribution on the applicability and effectiveness of the optimization; finally the genetic algorithm for integrated optimization of multi sensor station distribution using the good to the training exercise tasks based on actual in, and have achieved good military effect.

  11. A Genetic algorithm for evaluating the zeros (roots) of polynomial ...

    African Journals Online (AJOL)

    This paper presents a Genetic Algorithm software (which is a computational, search technique) for finding the zeros (roots) of any given polynomial function, and optimizing and solving N-dimensional systems of equations. The software is particularly useful since most of the classic schemes are not all embracing.

  12. Proposed genetic algorithms for construction site lay out

    NARCIS (Netherlands)

    Mawdesley, Michael J.; Al-Jibouri, Saad H.S.

    2003-01-01

    The positioning of temporary facilities on a construction site is an area of research which has been recognised as important but which has received relatively little attention. In this paper, a genetic algorithm is proposed to solve the problem in which m facilities are to be positioned to n

  13. Time-Delay System Identification Using Genetic Algorithm

    DEFF Research Database (Denmark)

    Yang, Zhenyu; Seested, Glen Thane

    2013-01-01

    problem through an identification approach using the real coded Genetic Algorithm (GA). The desired FOPDT/SOPDT model is directly identified based on the measured system's input and output data. In order to evaluate the quality and performance of this GA-based approach, the proposed method is compared...

  14. Non-Guard Interval based and Genetic Algorithm Assisted ...

    African Journals Online (AJOL)

    USER

    (Kshetrimayum, 2009). UWB technology is one of the promising solutions in terms of high-speed short-range wireless communication systems (Porcino and Hirt, 2003). Genetic algorithm (GA) is an effective search technique, which works on the Darwinian principle of natural selection called "survival of the fittest" (Man et al., ...

  15. A parallel genetic algorithm for the set partitioning problem

    Energy Technology Data Exchange (ETDEWEB)

    Levine, D.

    1996-12-31

    This paper describes a parallel genetic algorithm developed for the solution of the set partitioning problem- a difficult combinatorial optimization problem used by many airlines as a mathematical model for flight crew scheduling. The genetic algorithm is based on an island model where multiple independent subpopulations each run a steady-state genetic algorithm on their own subpopulation and occasionally fit strings migrate between the subpopulations. Tests on forty real-world set partitioning problems were carried out on up to 128 nodes of an IBM SP1 parallel computer. We found that performance, as measured by the quality of the solution found and the iteration on which it was found, improved as additional subpopulations were added to the computation. With larger numbers of subpopulations the genetic algorithm was regularly able to find the optimal solution to problems having up to a few thousand integer variables. In two cases, high- quality integer feasible solutions were found for problems with 36, 699 and 43,749 integer variables, respectively. A notable limitation we found was the difficulty solving problems with many constraints.

  16. Application of genetic algorithms to hydrogenated silicon clusters

    Indian Academy of Sciences (India)

    Unknown

    Genetic algorithms, as indicated before, require map- ping of all the system variables in a binary format. In case of a large number of variables, this leads to proce- ssing of large arrays containing 1s and 0s, which often substantially retards the computational speed. In addition, binary arithmetic has an implicit disadvantage ...

  17. Genetic Algorithm Procreation Operators for Alloy Nanoparticle Catalysts

    DEFF Research Database (Denmark)

    Lysgaard, Steen; Landis, David Dominic; Bligaard, Thomas

    2014-01-01

    focus on the surface properties. Based on a genetic algorithm (GA) we introduce and discuss efficient permutation operations that work by interchanging positions of elements depending on their local environment and position in the cluster. We discuss the fact that in order to be efficient, the operators...

  18. Classifying epilepsy diseases using artificial neural networks and genetic algorithm.

    Science.gov (United States)

    Koçer, Sabri; Canal, M Rahmi

    2011-08-01

    In this study, FFT analysis is applied to the EEG signals of the normal and patient subjects and the obtained FFT coefficients are used as inputs in Artificial Neural Network (ANN). The differences shown by the non-stationary random signals such as EEG signals in cases of health and sickness (epilepsy) were evaluated and tried to be analyzed under computer-supported conditions by using artificial neural networks. Multi-Layer Perceptron (MLP) architecture is used Levenberg-Marquardt (LM), Quickprop (QP), Delta-bar delta (DBD), Momentum and Conjugate gradient (CG) learning algorithms, and the best performance was tried to be attained by ensuring the optimization with the use of genetic algorithms of the weights, learning rates, neuron numbers of hidden layer in the training process. This study shows that the artificial neural network increases the classification performance using genetic algorithm.

  19. Parameter optimization in molecular dynamics simulations using a genetic algorithm

    Energy Technology Data Exchange (ETDEWEB)

    Angibaud, L. [Department ' Science and Analysis of Materials' (SAM), Centre de Recherche Public - Gabriel Lippmann, 41 rue du Brill, L-4422 Belvaux (Luxembourg); Briquet, L., E-mail: briquet@lippmann.lu [Department ' Science and Analysis of Materials' (SAM), Centre de Recherche Public - Gabriel Lippmann, 41 rue du Brill, L-4422 Belvaux (Luxembourg); Philipp, P.; Wirtz, T. [Department ' Science and Analysis of Materials' (SAM), Centre de Recherche Public - Gabriel Lippmann, 41 rue du Brill, L-4422 Belvaux (Luxembourg); Kieffer, J. [Department of Materials Science and Engineering, University of Michigan, Ann Arbor, MI 48109-2136 (United States)

    2011-07-15

    In this work, we introduce a genetic algorithm for the parameterization of the reactive force field developed by Kieffer . This potential includes directional covalent bonds and dispersion terms. Important features of this force field for simulating systems that undergo significant structural reorganization are (i) the ability to account for the redistribution of electron density upon ionization, formation, or breaking of bonds, through a charge transfer term, and (ii) the fact that the angular constraints dynamically adjust when a change in the coordination number of an atom occurs. In this paper, we present the implementation of the genetic algorithm into the existing code as well as the algorithm efficiency and preliminary results on Si-Si force field optimization. The parameters obtained by this method will be compared to existing parameter sets obtained by a trial-and-error process.

  20. Evolutionary Schema of Modeling Based on Genetic Algorithms

    Directory of Open Access Journals (Sweden)

    Stacewicz Paweł

    2015-03-01

    Full Text Available In this paper, I propose a populational schema of modeling that consists of: (a a linear AFSV schema (with four basic stages of abstraction, formalization, simplification, and verification, and (b a higher-level schema employing the genetic algorithm (with partially random procedures of mutation, crossover, and selection. The basic ideas of the proposed solution are as follows: (1 whole populations of models are considered at subsequent stages of the modeling process, (2 successive populations are subjected to the activity of genetic operators and undergo selection procedures, (3 the basis for selection is the evaluation function of the genetic algorithm (this function corresponds to the model verification criterion and reflects the goal of the model. The schema can be applied to automate the modeling of the mind/brain by means of artificial neural networks: the structure of each network is modified by genetic operators, modified networks undergo a learning cycle, and successive populations of networks are verified during the selection procedure. The whole process can be automated only partially, because it is the researcher who defines the evaluation function of the genetic algorithm.

  1. Optimization of solar air collector using genetic algorithm and artificial bee colony algorithm

    Energy Technology Data Exchange (ETDEWEB)

    Sencan Sahin, Arzu [Sueleyman Demirel University, Technology Faculty, Isparta (Turkey)

    2012-11-15

    Thermal performance of solar air collector depends on many parameters as inlet air temperature, air velocity, collector slope and properties related to collector. In this study, the effect of the different parameters which affect the performance of the solar air collector are investigated. In order to maximize the thermal performance of a solar air collector genetic algorithm (GA) and artificial bee colony algorithm (ABC) have been used. The results obtained indicate that GA and ABC algorithms can be applied successfully for the optimization of the thermal performance of solar air collector. (orig.)

  2. Genetic algorithm based reactive power dispatch for voltage stability improvement

    Energy Technology Data Exchange (ETDEWEB)

    Devaraj, D. [Department of Electrical and Electronics, Kalasalingam University, Krishnankoil 626 190 (India); Roselyn, J. Preetha [Department of Electrical and Electronics, SRM University, Kattankulathur 603 203, Chennai (India)

    2010-12-15

    Voltage stability assessment and control form the core function in a modern energy control centre. This paper presents an improved Genetic algorithm (GA) approach for voltage stability enhancement. The proposed technique is based on the minimization of the maximum of L-indices of load buses. Generator voltages, switchable VAR sources and transformer tap changers are used as optimization variables of this problem. The proposed approach permits the optimization variables to be represented in their natural form in the genetic population. For effective genetic processing, the crossover and mutation operators which can directly deal with the floating point numbers and integers are used. The proposed algorithm has been tested on IEEE 30-bus and IEEE 57-bus test systems and successful results have been obtained. (author)

  3. Genetic Algorithm Applied to the Eigenvalue Equalization Filtered-x LMS Algorithm (EE-FXLMS

    Directory of Open Access Journals (Sweden)

    Stephan P. Lovstedt

    2008-01-01

    Full Text Available The FXLMS algorithm, used extensively in active noise control (ANC, exhibits frequency-dependent convergence behavior. This leads to degraded performance for time-varying tonal noise and noise with multiple stationary tones. Previous work by the authors proposed the eigenvalue equalization filtered-x least mean squares (EE-FXLMS algorithm. For that algorithm, magnitude coefficients of the secondary path transfer function are modified to decrease variation in the eigenvalues of the filtered-x autocorrelation matrix, while preserving the phase, giving faster convergence and increasing overall attenuation. This paper revisits the EE-FXLMS algorithm, using a genetic algorithm to find magnitude coefficients that give the least variation in eigenvalues. This method overcomes some of the problems with implementing the EE-FXLMS algorithm arising from finite resolution of sampled systems. Experimental control results using the original secondary path model, and a modified secondary path model for both the previous implementation of EE-FXLMS and the genetic algorithm implementation are compared.

  4. An improved localization algorithm based on genetic algorithm in wireless sensor networks.

    Science.gov (United States)

    Peng, Bo; Li, Lei

    2015-04-01

    Wireless sensor network (WSN) are widely used in many applications. A WSN is a wireless decentralized structure network comprised of nodes, which autonomously set up a network. The node localization that is to be aware of position of the node in the network is an essential part of many sensor network operations and applications. The existing localization algorithms can be classified into two categories: range-based and range-free. The range-based localization algorithm has requirements on hardware, thus is expensive to be implemented in practice. The range-free localization algorithm reduces the hardware cost. Because of the hardware limitations of WSN devices, solutions in range-free localization are being pursued as a cost-effective alternative to more expensive range-based approaches. However, these techniques usually have higher localization error compared to the range-based algorithms. DV-Hop is a typical range-free localization algorithm utilizing hop-distance estimation. In this paper, we propose an improved DV-Hop algorithm based on genetic algorithm. Simulation results show that our proposed algorithm improves the localization accuracy compared with previous algorithms.

  5. The Ordered Clustered Travelling Salesman Problem: A Hybrid Genetic Algorithm

    Science.gov (United States)

    Ahmed, Zakir Hussain

    2014-01-01

    The ordered clustered travelling salesman problem is a variation of the usual travelling salesman problem in which a set of vertices (except the starting vertex) of the network is divided into some prespecified clusters. The objective is to find the least cost Hamiltonian tour in which vertices of any cluster are visited contiguously and the clusters are visited in the prespecified order. The problem is NP-hard, and it arises in practical transportation and sequencing problems. This paper develops a hybrid genetic algorithm using sequential constructive crossover, 2-opt search, and a local search for obtaining heuristic solution to the problem. The efficiency of the algorithm has been examined against two existing algorithms for some asymmetric and symmetric TSPLIB instances of various sizes. The computational results show that the proposed algorithm is very effective in terms of solution quality and computational time. Finally, we present solution to some more symmetric TSPLIB instances. PMID:24701148

  6. Packing Boxes into Multiple Containers Using Genetic Algorithm

    Science.gov (United States)

    Menghani, Deepak; Guha, Anirban

    2016-07-01

    Container loading problems have been studied extensively in the literature and various analytical, heuristic and metaheuristic methods have been proposed. This paper presents two different variants of a genetic algorithm framework for the three-dimensional container loading problem for optimally loading boxes into multiple containers with constraints. The algorithms are designed so that it is easy to incorporate various constraints found in real life problems. The algorithms are tested on data of standard test cases from literature and are found to compare well with the benchmark algorithms in terms of utilization of containers. This, along with the ability to easily incorporate a wide range of practical constraints, makes them attractive for implementation in real life scenarios.

  7. Genetic algorithms for optimal design and control of adaptive structures

    Science.gov (United States)

    Ribeiro, Rui; da Mota Silva, Suzana; Rodrigues, Jose D.; Vaz, Mario A. P.

    2000-06-01

    Future High Energy Physics experiments require the use of light and stable structures to support their most precise radiation detection elements. These large structures must be light, highly stable, stiff and radiation tolerant in an environment where external vibrations, high radiation levels, material aging, temperature and humidity gradients are not negligible. Unforeseen factors and the unknown result of the coupling of environmental conditions, together with external vibrations, may affect the position stability of the detectors and their support structures compromising their physics performance. Careful optimization of static and dynamic behavior must be an essential part of the engineering design. Genetic Algorithms (GA) belong to the group of probabilistic algorithms, combining elements of direct and stochastic search. They are more robust than existing directed search methods with the advantage of maintaining a population of potential solutions. There is a class of optimization problems for which Genetic Algorithms can be effectively applied. Among them are the ones related to shape control and optimal placement of sensors/actuators for active control of vibrations. In this paper these two problems are addressed and numerically investigated. The finite element method is used for the analysis of the dynamic characteristics. For the case of the optimal placement of sensors/actuators a performance index, proportional to the damping of the system in closed- loop, is used. Genetic algorithms prove their efficiency in this kind of optimization problems.

  8. Distributed Query Plan Generation Using Multiobjective Genetic Algorithm

    Directory of Open Access Journals (Sweden)

    Shina Panicker

    2014-01-01

    Full Text Available A distributed query processing strategy, which is a key performance determinant in accessing distributed databases, aims to minimize the total query processing cost. One way to achieve this is by generating efficient distributed query plans that involve fewer sites for processing a query. In the case of distributed relational databases, the number of possible query plans increases exponentially with respect to the number of relations accessed by the query and the number of sites where these relations reside. Consequently, computing optimal distributed query plans becomes a complex problem. This distributed query plan generation (DQPG problem has already been addressed using single objective genetic algorithm, where the objective is to minimize the total query processing cost comprising the local processing cost (LPC and the site-to-site communication cost (CC. In this paper, this DQPG problem is formulated and solved as a biobjective optimization problem with the two objectives being minimize total LPC and minimize total CC. These objectives are simultaneously optimized using a multiobjective genetic algorithm NSGA-II. Experimental comparison of the proposed NSGA-II based DQPG algorithm with the single objective genetic algorithm shows that the former performs comparatively better and converges quickly towards optimal solutions for an observed crossover and mutation probability.

  9. Distributed query plan generation using multiobjective genetic algorithm.

    Science.gov (United States)

    Panicker, Shina; Kumar, T V Vijay

    2014-01-01

    A distributed query processing strategy, which is a key performance determinant in accessing distributed databases, aims to minimize the total query processing cost. One way to achieve this is by generating efficient distributed query plans that involve fewer sites for processing a query. In the case of distributed relational databases, the number of possible query plans increases exponentially with respect to the number of relations accessed by the query and the number of sites where these relations reside. Consequently, computing optimal distributed query plans becomes a complex problem. This distributed query plan generation (DQPG) problem has already been addressed using single objective genetic algorithm, where the objective is to minimize the total query processing cost comprising the local processing cost (LPC) and the site-to-site communication cost (CC). In this paper, this DQPG problem is formulated and solved as a biobjective optimization problem with the two objectives being minimize total LPC and minimize total CC. These objectives are simultaneously optimized using a multiobjective genetic algorithm NSGA-II. Experimental comparison of the proposed NSGA-II based DQPG algorithm with the single objective genetic algorithm shows that the former performs comparatively better and converges quickly towards optimal solutions for an observed crossover and mutation probability.

  10. Improved multilayer OLED architecture using evolutionary genetic algorithm

    Energy Technology Data Exchange (ETDEWEB)

    Quirino, W.G. [LADOR - Laboratorio de Dispositivos Organicos, Dimat - Inmetro, Duque de Caxias, RJ (Brazil); Teixeira, K.C. [LADOR - Laboratorio de Dispositivos Organicos, Dimat - Inmetro, Duque de Caxias, RJ (Brazil); LOEM - Laboratorio de Optoeletronica Molecular, Physics Department, Pontifical Catholic University of Rio de Janeiro, 22453-900, Rio de Janeiro, RJ (Brazil); Legnani, C. [LADOR - Laboratorio de Dispositivos Organicos, Dimat - Inmetro, Duque de Caxias, RJ (Brazil); Calil, V.L. [LADOR - Laboratorio de Dispositivos Organicos, Dimat - Inmetro, Duque de Caxias, RJ (Brazil); LOEM - Laboratorio de Optoeletronica Molecular, Physics Department, Pontifical Catholic University of Rio de Janeiro, 22453-900, Rio de Janeiro, RJ (Brazil); Messer, B.; Neto, O.P. Vilela; Pacheco, M.A.C. [ICA - Laboratorio de Inteligencia Computacional Aplicada, Electrical Engineering Department, Pontifical Catholic University of Rio de Janeiro, 22451-900, Rio de Janeiro, RJ (Brazil); Cremona, M., E-mail: cremona@fis.puc-rio.b [LADOR - Laboratorio de Dispositivos Organicos, Dimat - Inmetro, Duque de Caxias, RJ (Brazil); LOEM - Laboratorio de Optoeletronica Molecular, Physics Department, Pontifical Catholic University of Rio de Janeiro, 22453-900, Rio de Janeiro, RJ (Brazil)

    2009-12-31

    Organic light-emitting diodes (OLEDs) constitute a new class of emissive devices, which present high efficiency and low voltage operation, among other advantages over current technology. Multilayer architecture (M-OLED) is generally used to optimize these devices, specially overcoming the suppression of light emission due to the exciton recombination near the metal layers. However, improvement in recombination, transport and charge injection can also be achieved by blending electron and hole transporting layers into the same one. Graded emissive region devices can provide promising results regarding quantum and power efficiency and brightness, as well. The massive number of possible model configurations, however, suggests that a search algorithm would be more suitable for this matter. In this work, multilayer OLEDs were simulated and fabricated using Genetic Algorithms (GAs) as evolutionary strategy to improve their efficiency. Genetic Algorithms are stochastic algorithms based on genetic inheritance and Darwinian strife to survival. In our simulations, it was assumed a 50 nm width graded region, divided into five equally sized layers. The relative concentrations of the materials within each layer were optimized to obtain the lower V/J{sup 0.5} ratio, where V is the applied voltage and J the current density. The best M-OLED architecture obtained by genetic algorithm presented a V/J{sup 0.5} ratio nearly 7% lower than the value reported in the literature. In order to check the experimental validity of the improved results obtained in the simulations, two M-OLEDs with different architectures were fabricated by thermal deposition in high vacuum environment. The results of the comparison between simulation and some experiments are presented and discussed.

  11. Genetic algorithms and their use in geophysical problems

    Science.gov (United States)

    Parker, Paul Bradley

    Genetic algorithms (GAs), global optimization methods that mimic Darwinian evolution are well suited to the nonlinear inverse problems of geophysics. A standard genetic algorithm selects the best or "fittest" models from a "population" and then applies operators such as crossover and mutation in order to combine the most successful characteristics of each model and produce fitter models. More sophisticated operators have been developed, but the standard GA usually provides a robust and efficient search. Although the choice of parameter settings such as crossover and mutation rate may depend largely on the type of problem being solved, numerous results show that certain parameter settings produce optimal performance for a wide range of problems and difficulties. In particular, a low (about half of the inverse of the population size) mutation rate is crucial for optimal results, but the choice of crossover method and rate do not seem to affect performance appreciably. Also, optimal efficiency is usually achieved with smaller (populations. Lastly, tournament selection appears to be the best choice of selection methods due to its simplicity and its autoscaling properties. However, if a proportional selection method is used such as roulette wheel selection, fitness scaling is a necessity, and a high scaling factor (>2.0) should be used for the best performance. Three case studies are presented in which genetic algorithms are used to invert for crustal parameters. The first is an inversion for basement depth at Yucca mountain using gravity data, the second an inversion for velocity structure in the crust of the south island of New Zealand using receiver functions derived from teleseismic events, and the third is a similar receiver function inversion for crustal velocities beneath the Mendocino Triple Junction region of Northern California. The inversions demonstrate that genetic algorithms are effective in solving problems with reasonably large numbers of free parameters

  12. Genetic algorithms and their use in Geophysical Problems

    Energy Technology Data Exchange (ETDEWEB)

    Parker, Paul B. [Univ. of California, Berkeley, CA (United States)

    1999-04-01

    Genetic algorithms (GAs), global optimization methods that mimic Darwinian evolution are well suited to the nonlinear inverse problems of geophysics. A standard genetic algorithm selects the best or ''fittest'' models from a ''population'' and then applies operators such as crossover and mutation in order to combine the most successful characteristics of each model and produce fitter models. More sophisticated operators have been developed, but the standard GA usually provides a robust and efficient search. Although the choice of parameter settings such as crossover and mutation rate may depend largely on the type of problem being solved, numerous results show that certain parameter settings produce optimal performance for a wide range of problems and difficulties. In particular, a low (about half of the inverse of the population size) mutation rate is crucial for optimal results, but the choice of crossover method and rate do not seem to affect performance appreciably. Optimal efficiency is usually achieved with smaller (< 50) populations. Lastly, tournament selection appears to be the best choice of selection methods due to its simplicity and its autoscaling properties. However, if a proportional selection method is used such as roulette wheel selection, fitness scaling is a necessity, and a high scaling factor (> 2.0) should be used for the best performance. Three case studies are presented in which genetic algorithms are used to invert for crustal parameters. The first is an inversion for basement depth at Yucca mountain using gravity data, the second an inversion for velocity structure in the crust of the south island of New Zealand using receiver functions derived from teleseismic events, and the third is a similar receiver function inversion for crustal velocities beneath the Mendocino Triple Junction region of Northern California. The inversions demonstrate that genetic algorithms are effective in solving problems

  13. A genetic algorithm used for solving one optimization problem

    Science.gov (United States)

    Shipacheva, E. N.; Petunin, A. A.; Berezin, I. M.

    2017-12-01

    A problem of minimizing the length of the blank run for a cutting tool during cutting of sheet materials into shaped blanks is discussed. This problem arises during the preparation of control programs for computerized numerical control (CNC) machines. A discrete model of the problem is analogous in setting to the generalized travelling salesman problem with limitations in the form of precursor conditions determined by the technological features of cutting. A certain variant of a genetic algorithm for solving this problem is described. The effect of the parameters of the developed algorithm on the solution result for the problem with limitations is investigated.

  14. Diagnosis support using Fuzzy Cognitive Maps combined with Genetic Algorithms.

    Science.gov (United States)

    Georgopoulos, Voula C; Stylios, Chrysotomos D

    2009-01-01

    A new hybrid modeling methodology to support medical diagnosis decisions is developed here. It extends previous work on Competitive Fuzzy Cognitive Maps for Medical Diagnosis Support Systems by complementing them with Genetic Algorithms Methods for concept interaction. The synergy of these methodologies is accomplished by a new proposed algorithm that leads to more dependable Advanced Medical Diagnosis Support Systems that are suitable to handle situations where the decisions are not clearly distinct. The technique developed here is applied successfully to model and test a differential diagnosis problem from the speech pathology area for the diagnosis of language impairments.

  15. Control of the lighting system using a genetic algorithm

    Directory of Open Access Journals (Sweden)

    Čongradac Velimir D.

    2012-01-01

    Full Text Available The manufacturing, distribution and use of electricity are of fundamental importance for the social life and they have the biggest influence on the environment associated with any human activity. The energy needed for building lighting makes up 20-40% of the total consumption. This paper displays the development of the mathematical model and genetic algorithm for the control of dimmable lighting on problems of regulating the level of internal lighting and increase of energetic efficiency using daylight. A series of experiments using the optimization algorithm on the realized model confirmed very high savings in electricity consumption.

  16. Thermoluminescence curves simulation using genetic algorithm with factorial design

    Science.gov (United States)

    Popko, E. A.; Weinstein, I. A.

    2016-05-01

    The evolutionary approach is an effective optimization tool for numeric analysis of thermoluminescence (TL) processes to assess the microparameters of kinetic models and to determine its effects on the shape of TL peaks. In this paper, the procedure for tuning of genetic algorithm (GA) is presented. This approach is based on multifactorial experiment and allows choosing intrinsic mechanisms of evolutionary operators which provide the most efficient algorithm performance. The proposed method is tested by considering the “one trap-one recombination center” (OTOR) model as an example and advantages for approximation of experimental TL curves are shown.

  17. Study on the Method of Face Detection Based on Chaos Genetic Algorithm Optimization AdaBoost Algorithm

    Directory of Open Access Journals (Sweden)

    Chai Yu

    2017-01-01

    Full Text Available Aiming at the problems that the traditional AdaBoost algorithm has complex feature computation, long training time and low detection rate, a method of face detection based on chaos genetic algorithm optimization adaBoost algorithm was proposed. Firstly, this algorithm uses the image color segmentation for coarse screening on the face image, in order to determine the human skin area. Secondly, the adaptive median filtering is applied to denoise the face image to improve the quality of the face image. Finally, the chaotic genetic algorithm is used to optimize the AdaBoost algorithm to achieve higher detection rate and detection speed. Compared with the traditional AdaBoost algorithm, the experimental results showed that the face detection method based on chaos genetic algorithm optimization AdaBoost algorithm proposed in this paper has a significant improvement in detection rate and detection speed.

  18. Genetic Algorithm for Initial Orbit Determination with Too Short Arc

    Science.gov (United States)

    Xin-ran, Li; Xin, Wang

    2017-01-01

    A huge quantity of too-short-arc (TSA) observational data have been obtained in sky surveys of space objects. However, reasonable results for the TSAs can hardly be obtained with the classical methods of initial orbit determination (IOD). In this paper, the IOD is reduced to a two-stage hierarchical optimization problem containing three variables for each stage. Using the genetic algorithm, a new method of the IOD for TSAs is established, through the selections of the optimized variables and the corresponding genetic operators for specific problems. Numerical experiments based on the real measurements show that the method can provide valid initial values for the follow-up work.

  19. Adaptive Process Control with Fuzzy Logic and Genetic Algorithms

    Science.gov (United States)

    Karr, C. L.

    1993-01-01

    Researchers at the U.S. Bureau of Mines have developed adaptive process control systems in which genetic algorithms (GA's) are used to augment fuzzy logic controllers (FLC's). GA's are search algorithms that rapidly locate near-optimum solutions to a wide spectrum of problems by modeling the search procedures of natural genetics. FLC's are rule based systems that efficiently manipulate a problem environment by modeling the 'rule-of-thumb' strategy used in human decision-making. Together, GA's and FLC's possess the capabilities necessary to produce powerful, efficient, and robust adaptive control systems. To perform efficiently, such control systems require a control element to manipulate the problem environment, an analysis element to recognize changes in the problem environment, and a learning element to adjust to the changes in the problem environment. Details of an overall adaptive control system are discussed. A specific laboratory acid-base pH system is used to demonstrate the ideas presented.

  20. Adaptive process control using fuzzy logic and genetic algorithms

    Science.gov (United States)

    Karr, C. L.

    1993-01-01

    Researchers at the U.S. Bureau of Mines have developed adaptive process control systems in which genetic algorithms (GA's) are used to augment fuzzy logic controllers (FLC's). GA's are search algorithms that rapidly locate near-optimum solutions to a wide spectrum of problems by modeling the search procedures of natural genetics. FLC's are rule based systems that efficiently manipulate a problem environment by modeling the 'rule-of-thumb' strategy used in human decision making. Together, GA's and FLC's possess the capabilities necessary to produce powerful, efficient, and robust adaptive control systems. To perform efficiently, such control systems require a control element to manipulate the problem environment, and a learning element to adjust to the changes in the problem environment. Details of an overall adaptive control system are discussed. A specific laboratory acid-base pH system is used to demonstrate the ideas presented.

  1. Stabilization of Electromagnetic Suspension System Behavior by Genetic Algorithm

    Directory of Open Access Journals (Sweden)

    Abbas Najar Khoda Bakhsh

    2012-07-01

    Full Text Available Electromagnetic suspension system with a nonlinear and unstable behavior, is used in maglev trains. In this paper a linear mathematical model of system is achieved and the state feedback method is used to improve the system stability. The control coefficients are tuned by two different methods, Riccati and a new method based on Genetic algorithm. In this new proposed method, we use Genetic algorithm to achieve the optimum values of control coefficients. The results of the system simulation by Matlab indicate the effectiveness of new proposed system. When a new reference of air gap is needed or a new external force is added, the proposed system could omit the vibration and shake of the train coupe and so, passengers feel more comfortable.

  2. Genetic algorithm for multiple-target-tracking data association

    Science.gov (United States)

    Carrier, Jean-Yves; Litva, John; Leung, Henry; Lo, Titus K. Y.

    1996-06-01

    The heart of any tracking system is its data association algorithm where measurements, received as sensor returns, are assigned to a track, or rejected as clutter. In this paper, we investigate the use of genetic algorithms (GA) for the multiple target tracking data association problem. GA are search methods based on the mechanics of natural selection and genetics. They have been proven theoretically and empirically robust in complex space searches by the founder J. H. Holland. Contrary to most optimization techniques, which seek to improve performance toward the optimum, GA find near-optimal solutions through parallel searches in the solution space. We propose to optimize a simplified version of the neural energy function proposed by Sengupta and Iltis in their network implementation of the joint probability data association. We follow an identical approach by first implementing a GA for the travelling salesperson problem based on Hopfield and Tank's neural network research.

  3. Genetic Algorithm Design of a 3D Printed Heat Sink

    Energy Technology Data Exchange (ETDEWEB)

    Wu, Tong [ORNL; Ozpineci, Burak [ORNL; Ayers, Curtis William [ORNL

    2016-01-01

    In this paper, a genetic algorithm- (GA-) based approach is discussed for designing heat sinks based on total heat generation and dissipation for a pre-specified size andshape. This approach combines random iteration processesand genetic algorithms with finite element analysis (FEA) to design the optimized heat sink. With an approach that prefers survival of the fittest , a more powerful heat sink can bedesigned which can cool power electronics more efficiently. Some of the resulting designs can only be 3D printed due totheir complexity. In addition to describing the methodology, this paper also includes comparisons of different cases to evaluate the performance of the newly designed heat sinkcompared to commercially available heat sinks.

  4. A Selection Process for Genetic Algorithm Using Clustering Analysis

    Directory of Open Access Journals (Sweden)

    Adam Chehouri

    2017-11-01

    Full Text Available This article presents a newly proposed selection process for genetic algorithms on a class of unconstrained optimization problems. The k-means genetic algorithm selection process (KGA is composed of four essential stages: clustering, membership phase, fitness scaling and selection. Inspired from the hypothesis that clustering the population helps to preserve a selection pressure throughout the evolution of the population, a membership probability index is assigned to each individual following the clustering phase. Fitness scaling converts the membership scores in a range suitable for the selection function which selects the parents of the next generation. Two versions of the KGA process are presented: using a fixed number of clusters K (KGAf and via an optimal partitioning Kopt (KGAo determined by two different internal validity indices. The performance of each method is tested on seven benchmark problems.

  5. Design of PID-type controllers using multiobjective genetic algorithms.

    Science.gov (United States)

    Herreros, Alberto; Baeyens, Enrique; Perán, José R

    2002-10-01

    The design of a PID controller is a multiobjective problem. A plant and a set of specifications to be satisfied are given. The designer has to adjust the parameters of the PID controller such that the feedback interconnection of the plant and the controller satisfies the specifications. These specifications are usually competitive and any acceptable solution requires a tradeoff among them. An approach for adjusting the parameters of a PID controller based on multiobjective optimization and genetic algorithms is presented in this paper. The MRCD (multiobjective robust control design) genetic algorithm has been employed. The approach can be easily generalized to design multivariable coupled and decentralized PID loops and has been successfully validated for a large number of experimental cases.

  6. Genetic algorithm application in optimization of wireless sensor networks.

    Science.gov (United States)

    Norouzi, Ali; Zaim, A Halim

    2014-01-01

    There are several applications known for wireless sensor networks (WSN), and such variety demands improvement of the currently available protocols and the specific parameters. Some notable parameters are lifetime of network and energy consumption for routing which play key role in every application. Genetic algorithm is one of the nonlinear optimization methods and relatively better option thanks to its efficiency for large scale applications and that the final formula can be modified by operators. The present survey tries to exert a comprehensive improvement in all operational stages of a WSN including node placement, network coverage, clustering, and data aggregation and achieve an ideal set of parameters of routing and application based WSN. Using genetic algorithm and based on the results of simulations in NS, a specific fitness function was achieved, optimized, and customized for all the operational stages of WSNs.

  7. Efficient Feedforward Linearization Technique Using Genetic Algorithms for OFDM Systems

    Directory of Open Access Journals (Sweden)

    García Paloma

    2010-01-01

    Full Text Available Feedforward is a linearization method that simultaneously offers wide bandwidth and good intermodulation distortion suppression; so it is a good choice for Orthogonal Frequency Division Multiplexing (OFDM systems. Feedforward structure consists of two loops, being necessary an accurate adjustment between them along the time, and when temperature, environmental, or operating changes are produced. Amplitude and phase imbalances of the circuit elements in both loops produce mismatched effects that lead to degrade its performance. A method is proposed to compensate these mismatches, introducing two complex coefficients calculated by means of a genetic algorithm. A full study is carried out to choose the optimal parameters of the genetic algorithm applied to wideband systems based on OFDM technologies, which are very sensitive to nonlinear distortions. The method functionality has been verified by means of simulation.

  8. Optimum Groove Location of Hydrodynamic Journal Bearing Using Genetic Algorithm

    Directory of Open Access Journals (Sweden)

    Lintu Roy

    2013-01-01

    Full Text Available This paper presents the various arrangements of grooving location of two-groove oil journal bearing for optimum performance. An attempt has been made to find out the effect of different configurations of two groove oil journal bearing by changing groove locations. Various groove angles that have been considered are 10°, 20°, and 30°. The Reynolds equation is solved numerically in a finite difference grid satisfying the appropriate boundary conditions. Determination of optimum performance is based on maximization of nondimensional load, flow coefficient, and mass parameter and minimization of friction variable using genetic algorithm. The results using genetic algorithm are compared with sequential quadratic programming (SQP. The two grooved bearings in general have grooves placed at diametrically opposite directions. However, the optimum groove locations, arrived at in the present work, are not diametrically opposite.

  9. Genetic Algorithm Based Hybrid Fuzzy System for Assessing Morningness

    Directory of Open Access Journals (Sweden)

    Animesh Biswas

    2014-01-01

    Full Text Available This paper describes a real life case example on the assessment process of morningness of individuals using genetic algorithm based hybrid fuzzy system. It is observed that physical and mental performance of human beings in different time slots of a day are majorly influenced by morningness orientation of those individuals. To measure the morningness of people various self-reported questionnaires were developed by different researchers in the past. Among them reduced version of Morningness-Eveningness Questionnaire is mostly accepted. Almost all of the linguistic terms used in questionnaires are fuzzily defined. So, assessing them in crisp environments with their responses does not seem to be justifiable. Fuzzy approach based research works for assessing morningness of people are very few in the literature. In this paper, genetic algorithm is used to tune the parameters of a Mamdani fuzzy inference model to minimize error with their predicted outputs for assessing morningness of people.

  10. Parametric analysis of architectural volumes through genetic algorithms

    Directory of Open Access Journals (Sweden)

    Pedro Salcedo Lagos

    2015-03-01

    Full Text Available During the last time, architectural design has developed partly due to new digital design techniques, which allow the generation of geometries based on the definition of initial parameters and the programming of formal relationship between them. Design processes based on these technologies allow to create shapes with the capacity to modify and adapt to multiple constrains or specific evaluation criteria, which raises the problem of identifying the best architectural solution. Several experiences have set up the utilization of genetic algorithm to face this problem. This paper demonstrates the possibility to implement a parametric analysis of architectural volumes with genetic algorithm, in order to combine functional, environmental and structural requirements, with an effective search method to select a variety of proper solutions through digital technologies.

  11. Optimising steel production schedules via a hierarchical genetic algorithm

    Directory of Open Access Journals (Sweden)

    Worapradya, Kiatkajohn

    2014-08-01

    Full Text Available This paper presents an effective scheduling in a steel-making continuous casting (SCC plant. The main contribution of this paper is the formulation of a new optimisation model that more closely represents real-world situations, and a hierarchical genetic algorithm (HGA tailored particularly for searching for an optimal SCC schedule. The optimisation model is developed by integrating two main planning phases of traditional scheduling: (1 planning cast sequence, and (2 scheduling of steel-making and timing of all jobs. A novel procedure is given for genetic algorithm (GA chromosome coding that maps Gantt chart and hierarchical chromosomes. The performance of the proposed methodology is illustrated and compared with a two-phase traditional scheduling and a standard GA toolbox. Both qualitative and quantitative performance measures are investigated.

  12. Path-oriented test cases generation based adaptive genetic algorithm.

    Science.gov (United States)

    Bao, Xiaoan; Xiong, Zijian; Zhang, Na; Qian, Junyan; Wu, Biao; Zhang, Wei

    2017-01-01

    The automatic generation of test cases oriented paths in an effective manner is a challenging problem for structural testing of software. The use of search-based optimization methods, such as genetic algorithms (GAs), has been proposed to handle this problem. This paper proposes an improved adaptive genetic algorithm (IAGA) for test cases generation by maintaining population diversity. It uses adaptive crossover rate and mutation rate in dynamic adjustment according to the differences between individual similarity and fitness values, which enhances the exploitation of searching global optimum. This novel approach is experimented and tested on a benchmark and six industrial programs. The experimental results confirm that the proposed method is efficient in generating test cases for path coverage.

  13. Exergetic optimization of turbofan engine with genetic algorithm method

    Energy Technology Data Exchange (ETDEWEB)

    Turan, Onder [Anadolu University, School of Civil Aviation (Turkey)], e-mail: onderturan@anadolu.edu.tr

    2011-07-01

    With the growth of passenger numbers, emissions from the aeronautics sector are increasing and the industry is now working on improving engine efficiency to reduce fuel consumption. The aim of this study is to present the use of genetic algorithms, an optimization method based on biological principles, to optimize the exergetic performance of turbofan engines. The optimization was carried out using exergy efficiency, overall efficiency and specific thrust of the engine as evaluation criteria and playing on pressure and bypass ratio, turbine inlet temperature and flight altitude. Results showed exergy efficiency can be maximized with higher altitudes, fan pressure ratio and turbine inlet temperature; the turbine inlet temperature is the most important parameter for increased exergy efficiency. This study demonstrated that genetic algorithms are effective in optimizing complex systems in a short time.

  14. A case study of a multiobjective recombinative genetic algorithm with coevolutionary sharing

    NARCIS (Netherlands)

    Neef, R.M.; Thierens, D.; Arciszewski, H.F.R.

    1999-01-01

    We present a multiobjective genetic algorithm that incorporates various genetic algorithm techniques that have been proven to be efficient and robust in their problem domain. More specifically, we integrate rank based selection, adaptive niching through coevolutionary sharing, elitist recombination,

  15. A Case Study of a Multiobjective Elitist Recombinative Genetic Algorithm with Coevolutionary Sharing

    NARCIS (Netherlands)

    Neef, R.M.; Thierens, D.; Arciszewski, H.F.R.

    1999-01-01

    We present a multiobjective genetic algorithm that incorporates various genetic algorithm techniques that have been proven to be efficient and robust in their problem domain. More specifically, we integrate rank based selection, adaptive niching through coevolutionary sharing, elitist recombination,

  16. Genomic multiple sequence alignments: refinement using a genetic algorithm

    Directory of Open Access Journals (Sweden)

    Lefkowitz Elliot J

    2005-08-01

    Full Text Available Abstract Background Genomic sequence data cannot be fully appreciated in isolation. Comparative genomics – the practice of comparing genomic sequences from different species – plays an increasingly important role in understanding the genotypic differences between species that result in phenotypic differences as well as in revealing patterns of evolutionary relationships. One of the major challenges in comparative genomics is producing a high-quality alignment between two or more related genomic sequences. In recent years, a number of tools have been developed for aligning large genomic sequences. Most utilize heuristic strategies to identify a series of strong sequence similarities, which are then used as anchors to align the regions between the anchor points. The resulting alignment is globally correct, but in many cases is suboptimal locally. We describe a new program, GenAlignRefine, which improves the overall quality of global multiple alignments by using a genetic algorithm to improve local regions of alignment. Regions of low quality are identified, realigned using the program T-Coffee, and then refined using a genetic algorithm. Because a better COFFEE (Consistency based Objective Function For alignmEnt Evaluation score generally reflects greater alignment quality, the algorithm searches for an alignment that yields a better COFFEE score. To improve the intrinsic slowness of the genetic algorithm, GenAlignRefine was implemented as a parallel, cluster-based program. Results We tested the GenAlignRefine algorithm by running it on a Linux cluster to refine sequences from a simulation, as well as refine a multiple alignment of 15 Orthopoxvirus genomic sequences approximately 260,000 nucleotides in length that initially had been aligned by Multi-LAGAN. It took approximately 150 minutes for a 40-processor Linux cluster to optimize some 200 fuzzy (poorly aligned regions of the orthopoxvirus alignment. Overall sequence identity increased only

  17. Evolutionary Image Enhancement Using Multi-Objective Genetic Algorithm

    OpenAIRE

    Dhirendra Pal Singh; Ashish Khare

    2013-01-01

    Image Processing is the art of examining, identifying and judging the significances of the Images. Image enhancement refers to attenuation, or sharpening, of image features such as edgels, boundaries, or contrast to make the processed image more useful for analysis. Image enhancement procedures utilize the computers to provide good and improved images for study by the human interpreters. In this paper we proposed a novel method that uses the Genetic Algorithm with Multi-objective criteria to ...

  18. Wind Farm Layout Optimization using Population Distributed Genetic Algorithms

    OpenAIRE

    Tangen, Helene

    2016-01-01

    Wind turbine technology is a promising source of renewable energy. However, the potential of wind turbine technology can not be utilized unless the wind farm layout is efficient. The challenge with wind farm layout optimization is that finding the optimal layout in the huge search space of different layouts is hard, if not impossible, to do analytically. Genetic algorithm techniques have been applied in many fields to solve non-linear optimization problems, and has shown promising results whe...

  19. Genetic Algorithms in Wireless Networking: Techniques, Applications, and Issues

    OpenAIRE

    Mehboob, Usama; Qadir, Junaid; Ali, Salman; Vasilakos, Athanasios

    2014-01-01

    In recent times, wireless access technology is becoming increasingly commonplace due to the ease of operation and installation of untethered wireless media. The design of wireless networking is challenging due to the highly dynamic environmental condition that makes parameter optimization a complex task. Due to the dynamic, and often unknown, operating conditions, modern wireless networking standards increasingly rely on machine learning and artificial intelligence algorithms. Genetic algorit...

  20. Multi-objective genetic algorithm for pseudoknotted RNA sequence design

    OpenAIRE

    Akito eTaneda

    2012-01-01

    RNA inverse folding is a computational technology for designing RNA sequences which fold into a user-specified secondary structure. Although pseudoknots are functionally important motifs in RNA structures, less reports concerning the inverse folding of pseudoknotted RNAs have been done compared to those for pseudoknot-free RNA design. In this paper, we present a new version of our multi-objective genetic algorithm (MOGA), MODENA, which we have previously proposed for pseudoknot-free RNA inver...

  1. Eddy current testing probe optimization using a parallel genetic algorithm

    Directory of Open Access Journals (Sweden)

    Dolapchiev Ivaylo

    2008-01-01

    Full Text Available This paper uses the developed parallel version of Michalewicz's Genocop III Genetic Algorithm (GA searching technique to optimize the coil geometry of an eddy current non-destructive testing probe (ECTP. The electromagnetic field is computed using FEMM 2D finite element code. The aim of this optimization was to determine coil dimensions and positions that improve ECTP sensitivity to physical properties of the tested devices.

  2. Optimization of heat pump using fuzzy logic and genetic algorithm

    Energy Technology Data Exchange (ETDEWEB)

    Sahin, Arzu Sencan [Sueleyman Demirel University, Technology Faculty, Isparta (Turkey); Kilic, Bayram; Kilic, Ulas [Bucak Emin Guelmez Vocational School, Mehmet Akif Ersoy University, Bucak (Turkey)

    2011-12-15

    Heat pumps offer economical alternatives of recovering heat from different sources for use in various industrial, commercial and residential applications. In this study, single-stage air-source vapor compression heat pump system has been optimized using genetic algorithm (GA) and fuzzy logic (FL). The necessary thermodynamic properties for optimization were calculated by FL. Thermodynamic properties obtained with FL were compared with actual results. Then, the optimum working conditions of heat pump system were determined by the GA. (orig.)

  3. Genetic Algorithms with Niching for Conceptual Design Studies

    CERN Document Server

    Ramberger, S

    1998-01-01

    The paper describes the use of genetic algorithms with the concept of niching for the conceptual design of superconducting magnets for the Large Hadron Collider, LHC at CERN. The method provides the designer with a number of possible solutions which can then be further optimized for field quality and manufacturability. Two 6 block dipole coils were found and proved to have advantages compared to the standard 5 block version.

  4. Optimization of the catch bench design using a genetic algorithm

    National Research Council Canada - National Science Library

    Ruvin Wijesinghe Dakshith You Greg

    2016-01-01

    .... This study proposes a novel methodology which calculates the optimum catch bench width by integrating the rockfall simulation model and genetic algorithm into a Simulation-Optimization Model The proposed methodology is useful when used to determine the minimum catch bench width, or the maximum overall slope angle, insuring that a sufficient factor of safety of the slope is included while maximizing the overall profitability of the open pit mine.

  5. Synthesizing Sierpinski Antenna by Genetic Algorithm and Swarm Optimization

    Directory of Open Access Journals (Sweden)

    Z. Raida

    2008-12-01

    Full Text Available The paper discusses the synthesis of the Sierpinski antenna operating at three prescribed frequencies: 0.9 GHz, 1.8 GHz (both GSM and 2.4 GHz (Bluetooth. In order to synthesize the antenna, a genetic algorithm and a particle swarm optimization were used. The numerical model of the antenna was developed in Zeland IE3D, optimization scripts were programmed in MATLAB. Results of both the optimization methods are compared and experimentally verified.

  6. RCQ-GA: RDF Chain Query Optimization Using Genetic Algorithms

    Science.gov (United States)

    Hogenboom, Alexander; Milea, Viorel; Frasincar, Flavius; Kaymak, Uzay

    The application of Semantic Web technologies in an Electronic Commerce environment implies a need for good support tools. Fast query engines are needed for efficient querying of large amounts of data, usually represented using RDF. We focus on optimizing a special class of SPARQL queries, the so-called RDF chain queries. For this purpose, we devise a genetic algorithm called RCQ-GA that determines the order in which joins need to be performed for an efficient evaluation of RDF chain queries. The approach is benchmarked against a two-phase optimization algorithm, previously proposed in literature. The more complex a query is, the more RCQ-GA outperforms the benchmark in solution quality, execution time needed, and consistency of solution quality. When the algorithms are constrained by a time limit, the overall performance of RCQ-GA compared to the benchmark further improves.

  7. Use of genetic algorithms for solving problems of optimal cutting

    Directory of Open Access Journals (Sweden)

    Sergievskiy Maxim

    2016-01-01

    Full Text Available Cutting and packing problem is one of the most common optimization problem. Even a small space or material savings allow obtaining substantial advantages on an industrial scale. This paper proposes the genetic algorithm to solve this problem. It includes multipoint operators of crossing, mutation and selection. To use these operators, the special encoding of cutting card is applied, that can be transformed to the real coordinates by using decoder. This is a block typed decoder, which substitution strategy is “first-fit”. Efficiency of the solutions, obtained by the algorithm, depends on its parameters and on dimension of a task, but on average it decreases under the logarithmic law from dimension of a task. Temporary complexity of algorithm shows square dependence on the task dimension.

  8. Solving the Dial-a-Ride Problem using Genetic Algorithms

    DEFF Research Database (Denmark)

    Jørgensen, Rene Munk; Larsen, Jesper; Bergvinsdottir, Kristin Berg

    2007-01-01

    In the Dial-a-Ride problem (DARP), customers request transportation from an operator. A request consists of a specified pickup location and destination location along with a desired departure or arrival time and capacity demand. The aim of DARP is to minimize transportation cost while satisfying...... customer service level constraints (Quality of Service). In this paper, we present a genetic algorithm (GA) for solving the DARP. The algorithm is based on the classical cluster-first, route-second approach, where it alternates between assigning customers to vehicles using a GA and solving independent...... routing problems for the vehicles using a routing heuristic. The algorithm is implemented in Java and tested on publicly available data sets. The new solution method has achieved solutions comparable with the current state-of-the-art methods....

  9. Optimal recombination in genetic algorithms for flowshop scheduling problems

    Science.gov (United States)

    Kovalenko, Julia

    2016-10-01

    The optimal recombination problem consists in finding the best possible offspring as a result of a recombination operator in a genetic algorithm, given two parent solutions. We prove NP-hardness of the optimal recombination for various variants of the flowshop scheduling problem with makespan criterion and criterion of maximum lateness. An algorithm for solving the optimal recombination problem for permutation flowshop problems is built, using enumeration of prefect matchings in a special bipartite graph. The algorithm is adopted for the classical flowshop scheduling problem and for the no-wait flowshop problem. It is shown that the optimal recombination problem for the permutation flowshop scheduling problem is solvable in polynomial time for almost all pairs of parent solutions as the number of jobs tends to infinity.

  10. Design of an acoustic metamaterial lens using genetic algorithms.

    Science.gov (United States)

    Li, Dennis; Zigoneanu, Lucian; Popa, Bogdan-Ioan; Cummer, Steven A

    2012-10-01

    The present work demonstrates a genetic algorithm approach to optimizing the effective material parameters of an acoustic metamaterial. The target device is an acoustic gradient index (GRIN) lens in air, which ideally possesses a maximized index of refraction, minimized frequency dependence of the material properties, and minimized acoustic impedance mismatch. Applying this algorithm results in complex designs with certain common features, and effective material properties that are better than those present in previous designs. After modifying the optimized unit cell designs to make them suitable for fabrication, a two-dimensional lens was built and experimentally tested. Its performance was in good agreement with simulations. Overall, the optimization approach was able to improve the refractive index but at the cost of increased frequency dependence. The optimal solutions found by the algorithm provide a numerical description of how the material parameters compete with one another and thus describes the level of performance achievable in the GRIN lens.

  11. A genetic algorithm to reduce stream channel cross section data

    Science.gov (United States)

    Berenbrock, C.

    2006-01-01

    A genetic algorithm (GA) was used to reduce cross section data for a hypothetical example consisting of 41 data points and for 10 cross sections on the Kootenai River. The number of data points for the Kootenai River cross sections ranged from about 500 to more than 2,500. The GA was applied to reduce the number of data points to a manageable dataset because most models and other software require fewer than 100 data points for management, manipulation, and analysis. Results indicated that the program successfully reduced the data. Fitness values from the genetic algorithm were lower (better) than those in a previous study that used standard procedures of reducing the cross section data. On average, fitnesses were 29 percent lower, and several were about 50 percent lower. Results also showed that cross sections produced by the genetic algorithm were representative of the original section and that near-optimal results could be obtained in a single run, even for large problems. Other data also can be reduced in a method similar to that for cross section data.

  12. Design of PID Controller Simulator based on Genetic Algorithm

    Directory of Open Access Journals (Sweden)

    Fahri VATANSEVER

    2013-08-01

    Full Text Available PID (Proportional Integral and Derivative controllers take an important place in the field of system controlling. Various methods such as Ziegler-Nichols, Cohen-Coon, Chien Hrones Reswick (CHR and Wang-Juang-Chan are available for the design of such controllers benefiting from the system time and frequency domain data. These controllers are in compliance with system properties under certain criteria suitable to the system. Genetic algorithms have become widely used in control system applications in parallel to the advances in the field of computer and artificial intelligence. In this study, PID controller designs have been carried out by means of classical methods and genetic algorithms and comparative results have been analyzed. For this purpose, a graphical user interface program which can be used for educational purpose has been developed. For the definite (entered transfer functions, the suitable P, PI and PID controller coefficients have calculated by both classical methods and genetic algorithms and many parameters and responses of the systems have been compared and presented numerically and graphically

  13. Genetic Algorithm Optimizes Q-LAW Control Parameters

    Science.gov (United States)

    Lee, Seungwon; von Allmen, Paul; Petropoulos, Anastassios; Terrile, Richard

    2008-01-01

    A document discusses a multi-objective, genetic algorithm designed to optimize Lyapunov feedback control law (Q-law) parameters in order to efficiently find Pareto-optimal solutions for low-thrust trajectories for electronic propulsion systems. These would be propellant-optimal solutions for a given flight time, or flight time optimal solutions for a given propellant requirement. The approximate solutions are used as good initial solutions for high-fidelity optimization tools. When the good initial solutions are used, the high-fidelity optimization tools quickly converge to a locally optimal solution near the initial solution. Q-law control parameters are represented as real-valued genes in the genetic algorithm. The performances of the Q-law control parameters are evaluated in the multi-objective space (flight time vs. propellant mass) and sorted by the non-dominated sorting method that assigns a better fitness value to the solutions that are dominated by a fewer number of other solutions. With the ranking result, the genetic algorithm encourages the solutions with higher fitness values to participate in the reproduction process, improving the solutions in the evolution process. The population of solutions converges to the Pareto front that is permitted within the Q-law control parameter space.

  14. MAC Protocol for Ad Hoc Networks Using a Genetic Algorithm

    Science.gov (United States)

    Elizarraras, Omar; Panduro, Marco; Méndez, Aldo L.

    2014-01-01

    The problem of obtaining the transmission rate in an ad hoc network consists in adjusting the power of each node to ensure the signal to interference ratio (SIR) and the energy required to transmit from one node to another is obtained at the same time. Therefore, an optimal transmission rate for each node in a medium access control (MAC) protocol based on CSMA-CDMA (carrier sense multiple access-code division multiple access) for ad hoc networks can be obtained using evolutionary optimization. This work proposes a genetic algorithm for the transmission rate election considering a perfect power control, and our proposition achieves improvement of 10% compared with the scheme that handles the handshaking phase to adjust the transmission rate. Furthermore, this paper proposes a genetic algorithm that solves the problem of power combining, interference, data rate, and energy ensuring the signal to interference ratio in an ad hoc network. The result of the proposed genetic algorithm has a better performance (15%) compared to the CSMA-CDMA protocol without optimizing. Therefore, we show by simulation the effectiveness of the proposed protocol in terms of the throughput. PMID:25140339

  15. Genetic algorithm based optimization of advanced solar cell designs modeled in Silvaco AtlasTM

    OpenAIRE

    Utsler, James

    2006-01-01

    A genetic algorithm was used to optimize the power output of multi-junction solar cells. Solar cell operation was modeled using the Silvaco ATLASTM software. The output of the ATLASTM simulation runs served as the input to the genetic algorithm. The genetic algorithm was run as a diffusing computation on a network of eighteen dual processor nodes. Results showed that the genetic algorithm produced better power output optimizations when compared with the results obtained using the hill cli...

  16. Modified Multi-Population Genetic Algorithm for Yeast Fed-batch Cultivation Parameter Identification

    Directory of Open Access Journals (Sweden)

    Angelova M.

    2009-12-01

    Full Text Available In this work, a modified multi-population genetic algorithm is developed for the purpose of parameter identification of fermentation process model. Modified multi-population genetic algorithm is similar to the multi-population one and its development is instigated by modified genetic algorithm, similar to simple one. A comparison of four types of genetic algorithms, namely simple, modified, multipopulation and modified multi-population is presented for parameter identification of a fed-batch cultivation of Saccharomyces cerevisiae

  17. APPLICATION OF GENETIC ALGORITHMS FOR ROBUST PARAMETER OPTIMIZATION

    Directory of Open Access Journals (Sweden)

    N. Belavendram

    2010-12-01

    Full Text Available Parameter optimization can be achieved by many methods such as Monte-Carlo, full, and fractional factorial designs. Genetic algorithms (GA are fairly recent in this respect but afford a novel method of parameter optimization. In GA, there is an initial pool of individuals each with its own specific phenotypic trait expressed as a ‘genetic chromosome’. Different genes enable individuals with different fitness levels to reproduce according to natural reproductive gene theory. This reproduction is established in terms of selection, crossover and mutation of reproducing genes. The resulting child generation of individuals has a better fitness level akin to natural selection, namely evolution. Populations evolve towards the fittest individuals. Such a mechanism has a parallel application in parameter optimization. Factors in a parameter design can be expressed as a genetic analogue in a pool of sub-optimal random solutions. Allowing this pool of sub-optimal solutions to evolve over several generations produces fitter generations converging to a pre-defined engineering optimum. In this paper, a genetic algorithm is used to study a seven factor non-linear equation for a Wheatstone bridge as the equation to be optimized. A comparison of the full factorial design against a GA method shows that the GA method is about 1200 times faster in finding a comparable solution.

  18. A Parallel Genetic Algorithm to Discover Patterns in Genetic Markers that Indicate Predisposition to Multifactorial Disease

    Science.gov (United States)

    Rausch, Tobias; Thomas, Alun; Camp, Nicola J.; Cannon-Albright, Lisa A.; Facelli, Julio C.

    2008-01-01

    This paper describes a novel algorithm to analyze genetic linkage data using pattern recognition techniques and genetic algorithms (GA). The method allows a search for regions of the chromosome that may contain genetic variations that jointly predispose individuals for a particular disease. The method uses correlation analysis, filtering theory and genetic algorithms (GA) to achieve this goal. Because current genome scans use from hundreds to hundreds of thousands of markers, two versions of the method have been implemented. The first is an exhaustive analysis version that can be used to visualize, explore, and analyze small genetic data sets for two marker correlations; the second is a GA version, which uses a parallel implementation allowing searches of higher-order correlations in large data sets. Results on simulated data sets indicate that the method can be informative in the identification of major disease loci and gene-gene interactions in genome-wide linkage data and that further exploration of these techniques is justified. The results presented for both variants of the method show that it can help genetic epidemiologists to identify promising combinations of genetic factors that might predispose to complex disorders. In particular, the correlation analysis of IBD expression patterns might hint to possible gene-gene interactions and the filtering might be a fruitful approach to distinguish true correlation signals from noise. PMID:18547558

  19. DNA based random key generation and management for OTP encryption.

    Science.gov (United States)

    Zhang, Yunpeng; Liu, Xin; Sun, Manhui

    2017-09-01

    One-time pad (OTP) is a principle of key generation applied to the stream ciphering method which offers total privacy. The OTP encryption scheme has proved to be unbreakable in theory, but difficult to realize in practical applications. Because OTP encryption specially requires the absolute randomness of the key, its development has suffered from dense constraints. DNA cryptography is a new and promising technology in the field of information security. DNA chromosomes storing capabilities can be used as one-time pad structures with pseudo-random number generation and indexing in order to encrypt the plaintext messages. In this paper, we present a feasible solution to the OTP symmetric key generation and transmission problem with DNA at the molecular level. Through recombinant DNA technology, by using only sender-receiver known restriction enzymes to combine the secure key represented by DNA sequence and the T vector, we generate the DNA bio-hiding secure key and then place the recombinant plasmid in implanted bacteria for secure key transmission. The designed bio experiments and simulation results show that the security of the transmission of the key is further improved and the environmental requirements of key transmission are reduced. Analysis has demonstrated that the proposed DNA-based random key generation and management solutions are marked by high security and usability. Published by Elsevier B.V.

  20. Using Genetic Algorithms for Navigation Planning in Dynamic Environments

    Directory of Open Access Journals (Sweden)

    Ferhat Uçan

    2012-01-01

    Full Text Available Navigation planning can be considered as a combination of searching and executing the most convenient flight path from an initial waypoint to a destination waypoint. Generally the aim is to follow the flight path, which provides minimum fuel consumption for the air vehicle. For dynamic environments, constraints change dynamically during flight. This is a special case of dynamic path planning. As the main concern of this paper is flight planning, the conditions and objectives that are most probable to be used in navigation problem are considered. In this paper, the genetic algorithm solution of the dynamic flight planning problem is explained. The evolutionary dynamic navigation planning algorithm is developed for compensating the existing deficiencies of the other approaches. The existing fully dynamic algorithms process unit changes to topology one modification at a time, but when there are several such operations occurring in the environment simultaneously, the algorithms are quite inefficient. The proposed algorithm may respond to the concurrent constraint updates in a shorter time for dynamic environment. The most secure navigation of the air vehicle is planned and executed so that the fuel consumption is minimum.

  1. Swarm, genetic and evolutionary programming algorithms applied to multiuser detection

    Directory of Open Access Journals (Sweden)

    Paul Jean Etienne Jeszensky

    2005-02-01

    Full Text Available In this paper, the particles swarm optimization technique, recently published in the literature, and applied to Direct Sequence/Code Division Multiple Access systems (DS/CDMA with multiuser detection (MuD is analyzed, evaluated and compared. The Swarm algorithm efficiency when applied to the DS-CDMA multiuser detection (Swarm-MuD is compared through the tradeoff performance versus computational complexity, being the complexity expressed in terms of the number of necessary operations in order to reach the performance obtained through the optimum detector or the Maximum Likelihood detector (ML. The comparison is accomplished among the genetic algorithm, evolutionary programming with cloning and Swarm algorithm under the same simulation basis. Additionally, it is proposed an heuristics-MuD complexity analysis through the number of computational operations. Finally, an analysis is carried out for the input parameters of the Swarm algorithm in the attempt to find the optimum parameters (or almost-optimum for the algorithm applied to the MuD problem.

  2. A Multi-Objective Genetic Algorithm for Outlier Removal.

    Science.gov (United States)

    Nahum, Oren E; Yosipof, Abraham; Senderowitz, Hanoch

    2015-12-28

    Quantitative structure activity relationship (QSAR) or quantitative structure property relationship (QSPR) models are developed to correlate activities for sets of compounds with their structure-derived descriptors by means of mathematical models. The presence of outliers, namely, compounds that differ in some respect from the rest of the data set, compromise the ability of statistical methods to derive QSAR models with good prediction statistics. Hence, outliers should be removed from data sets prior to model derivation. Here we present a new multi-objective genetic algorithm for the identification and removal of outliers based on the k nearest neighbors (kNN) method. The algorithm was used to remove outliers from three different data sets of pharmaceutical interest (logBBB, factor 7 inhibitors, and dihydrofolate reductase inhibitors), and its performances were compared with those of five other methods for outlier removal. The results suggest that the new algorithm provides filtered data sets that (1) better maintain the internal diversity of the parent data sets and (2) give rise to QSAR models with much better prediction statistics. Equally good filtered data sets in terms of these metrics were obtained when another objective function was added to the algorithm (termed "preservation"), forcing it to remove certain compounds with low probability only. This option is highly useful when specific compounds should be preferably kept in the final data set either because they have favorable activities or because they represent interesting molecular scaffolds. We expect this new algorithm to be useful in future QSAR applications.

  3. Global structual optimizations of surface systems with a genetic algorithm

    Energy Technology Data Exchange (ETDEWEB)

    Chuang, Feng-Chuan [Iowa State Univ., Ames, IA (United States)

    2005-01-01

    Global structural optimizations with a genetic algorithm were performed for atomic cluster and surface systems including aluminum atomic clusters, Si magic clusters on the Si(111) 7 x 7 surface, silicon high-index surfaces, and Ag-induced Si(111) reconstructions. First, the global structural optimizations of neutral aluminum clusters Aln algorithm in combination with tight-binding and first-principles calculations were performed to study the structures of magic clusters on the Si(111) 7 x 7 surface. Extensive calculations show that the magic cluster observed in scanning tunneling microscopy (STM) experiments consist of eight Si atoms. Simulated STM images of the Si magic cluster exhibit a ring-like feature similar to STM experiments. Third, a genetic algorithm coupled with a highly optimized empirical potential were used to determine the lowest energy structure of high-index semiconductor surfaces. The lowest energy structures of Si(105) and Si(114) were determined successfully. The results of Si(105) and Si(114) are reported within the framework of highly optimized empirical potential and first-principles calculations. Finally, a genetic algorithm coupled with Si and Ag tight-binding potentials were used to search for Ag-induced Si(111) reconstructions at various Ag and Si coverages. The optimized structural models of √3 x √3, 3 x 1, and 5 x 2 phases were reported using first-principles calculations. A novel model is found to have lower surface energy than the proposed double-honeycomb chained (DHC) model both for Au/Si(111) 5 x 2 and Ag/Si(111) 5 x 2 systems.

  4. Optimization of Antennas using a Hybrid Genetic-Algorithm Space-Mapping Algorithm

    DEFF Research Database (Denmark)

    Pantoja, M.F.; Bretones, A.R.; Meincke, Peter

    2006-01-01

    A hybrid global-local optimization technique for the design of antennas is presented. It consists of the subsequent application of a Genetic Algorithm (GA) that employs coarse models in the simulations and a space mapping (SM) that refines the solution found in the previous stage. The technique i...... is particularly suited to optimization problems for which long computational times are required to achieve accurate solutions....

  5. Automatic Data Filter Customization Using a Genetic Algorithm

    Science.gov (United States)

    Mandrake, Lukas

    2013-01-01

    This work predicts whether a retrieval algorithm will usefully determine CO2 concentration from an input spectrum of GOSAT (Greenhouse Gases Observing Satellite). This was done to eliminate needless runtime on atmospheric soundings that would never yield useful results. A space of 50 dimensions was examined for predictive power on the final CO2 results. Retrieval algorithms are frequently expensive to run, and wasted effort defeats requirements and expends needless resources. This algorithm could be used to help predict and filter unneeded runs in any computationally expensive regime. Traditional methods such as the Fischer discriminant analysis and decision trees can attempt to predict whether a sounding will be properly processed. However, this work sought to detect a subsection of the dimensional space that can be simply filtered out to eliminate unwanted runs. LDAs (linear discriminant analyses) and other systems examine the entire data and judge a "best fit," giving equal weight to complex and problematic regions as well as simple, clear-cut regions. In this implementation, a genetic space of "left" and "right" thresholds outside of which all data are rejected was defined. These left/right pairs are created for each of the 50 input dimensions. A genetic algorithm then runs through countless potential filter settings using a JPL computer cluster, optimizing the tossed-out data s yield (proper vs. improper run removal) and number of points tossed. This solution is robust to an arbitrary decision boundary within the data and avoids the global optimization problem of whole-dataset fitting using LDA or decision trees. It filters out runs that would not have produced useful CO2 values to save needless computation. This would be an algorithmic preprocessing improvement to any computationally expensive system.

  6. A Parallel Genetic Algorithm for Automated Electronic Circuit Design

    Science.gov (United States)

    Long, Jason D.; Colombano, Silvano P.; Haith, Gary L.; Stassinopoulos, Dimitris

    2000-01-01

    Parallelized versions of genetic algorithms (GAs) are popular primarily for three reasons: the GA is an inherently parallel algorithm, typical GA applications are very compute intensive, and powerful computing platforms, especially Beowulf-style computing clusters, are becoming more affordable and easier to implement. In addition, the low communication bandwidth required allows the use of inexpensive networking hardware such as standard office ethernet. In this paper we describe a parallel GA and its use in automated high-level circuit design. Genetic algorithms are a type of trial-and-error search technique that are guided by principles of Darwinian evolution. Just as the genetic material of two living organisms can intermix to produce offspring that are better adapted to their environment, GAs expose genetic material, frequently strings of 1s and Os, to the forces of artificial evolution: selection, mutation, recombination, etc. GAs start with a pool of randomly-generated candidate solutions which are then tested and scored with respect to their utility. Solutions are then bred by probabilistically selecting high quality parents and recombining their genetic representations to produce offspring solutions. Offspring are typically subjected to a small amount of random mutation. After a pool of offspring is produced, this process iterates until a satisfactory solution is found or an iteration limit is reached. Genetic algorithms have been applied to a wide variety of problems in many fields, including chemistry, biology, and many engineering disciplines. There are many styles of parallelism used in implementing parallel GAs. One such method is called the master-slave or processor farm approach. In this technique, slave nodes are used solely to compute fitness evaluations (the most time consuming part). The master processor collects fitness scores from the nodes and performs the genetic operators (selection, reproduction, variation, etc.). Because of dependency

  7. Academic Training: Evolutionary Heuristic Optimization: Genetic Algorithms and Estimation of Distribution Algorithms - Lecture series

    CERN Multimedia

    Françoise Benz

    2004-01-01

    ACADEMIC TRAINING LECTURE REGULAR PROGRAMME 1, 2, 3 and 4 June From 11:00 hrs to 12:00 hrs - Main Auditorium bldg. 500 Evolutionary Heuristic Optimization: Genetic Algorithms and Estimation of Distribution Algorithms V. Robles Forcada and M. Perez Hernandez / Univ. de Madrid, Spain In the real world, there exist a huge number of problems that require getting an optimum or near-to-optimum solution. Optimization can be used to solve a lot of different problems such as network design, sets and partitions, storage and retrieval or scheduling. On the other hand, in nature, there exist many processes that seek a stable state. These processes can be seen as natural optimization processes. Over the last 30 years several attempts have been made to develop optimization algorithms, which simulate these natural optimization processes. These attempts have resulted in methods such as Simulated Annealing, based on natural annealing processes or Evolutionary Computation, based on biological evolution processes. Geneti...

  8. Academic Training: Evolutionary Heuristic Optimization: Genetic Algorithms and Estimation of Distribution Algorithms - Lecture serie

    CERN Multimedia

    Françoise Benz

    2004-01-01

    ENSEIGNEMENT ACADEMIQUE ACADEMIC TRAINING Françoise Benz 73127 academic.training@cern.ch ACADEMIC TRAINING LECTURE REGULAR PROGRAMME 1, 2, 3 and 4 June From 11:00 hrs to 12:00 hrs - Main Auditorium bldg. 500 Evolutionary Heuristic Optimization: Genetic Algorithms and Estimation of Distribution Algorithms V. Robles Forcada and M. Perez Hernandez / Univ. de Madrid, Spain In the real world, there exist a huge number of problems that require getting an optimum or near-to-optimum solution. Optimization can be used to solve a lot of different problems such as network design, sets and partitions, storage and retrieval or scheduling. On the other hand, in nature, there exist many processes that seek a stable state. These processes can be seen as natural optimization processes. Over the last 30 years several attempts have been made to develop optimization algorithms, which simulate these natural optimization processes. These attempts have resulted in methods such as Simulated Annealing, based on nat...

  9. Comparison of genetic algorithm and imperialist competitive algorithms in predicting bed load transport in clean pipe.

    Science.gov (United States)

    Ebtehaj, Isa; Bonakdari, Hossein

    2014-01-01

    The existence of sediments in wastewater greatly affects the performance of the sewer and wastewater transmission systems. Increased sedimentation in wastewater collection systems causes problems such as reduced transmission capacity and early combined sewer overflow. The article reviews the performance of the genetic algorithm (GA) and imperialist competitive algorithm (ICA) in minimizing the target function (mean square error of observed and predicted Froude number). To study the impact of bed load transport parameters, using four non-dimensional groups, six different models have been presented. Moreover, the roulette wheel selection method is used to select the parents. The ICA with root mean square error (RMSE) = 0.007, mean absolute percentage error (MAPE) = 3.5% show better results than GA (RMSE = 0.007, MAPE = 5.6%) for the selected model. All six models return better results than the GA. Also, the results of these two algorithms were compared with multi-layer perceptron and existing equations.

  10. Analysis of six stages supply chain management in inventory optimization for warehouse with artificial bee colony algorithm using genetic algorithm

    Directory of Open Access Journals (Sweden)

    Ajay Singh Yadav

    2017-09-01

    Full Text Available The purpose of the proposed study is to give a new dimension on warehouse with Artificial bee colony algorithm using genetic algorithm processes in six stages - 11 member supply chain in inventory optimization to describe the certain and uncertain market demand which is based on supply reliability and to develop more realistic and more flexible models. we hope that the proposed study has a great potential to solve various practical tribulations related to the warehouse using genetic algorithm processes in six stages - 11 member supply chain in inventory optimization and also provide a general review for the application of soft computing techniques like genetic algorithms to use for improve the effectiveness and efficiency for various aspect of warehouse with Artificial bee colony algorithm using genetic algorithm.

  11. Optimal Design of Passive Power Filters Based on Pseudo-parallel Genetic Algorithm

    Science.gov (United States)

    Li, Pei; Li, Hongbo; Gao, Nannan; Niu, Lin; Guo, Liangfeng; Pei, Ying; Zhang, Yanyan; Xu, Minmin; Chen, Kerui

    2017-05-01

    The economic costs together with filter efficiency are taken as targets to optimize the parameter of passive filter. Furthermore, the method of combining pseudo-parallel genetic algorithm with adaptive genetic algorithm is adopted in this paper. In the early stages pseudo-parallel genetic algorithm is introduced to increase the population diversity, and adaptive genetic algorithm is used in the late stages to reduce the workload. At the same time, the migration rate of pseudo-parallel genetic algorithm is improved to change with population diversity adaptively. Simulation results show that the filter designed by the proposed method has better filtering effect with lower economic cost, and can be used in engineering.

  12. Feature Selection for Image Retrieval based on Genetic Algorithm

    Directory of Open Access Journals (Sweden)

    Preeti Kushwaha

    2016-12-01

    Full Text Available This paper describes the development and implementation of feature selection for content based image retrieval. We are working on CBIR system with new efficient technique. In this system, we use multi feature extraction such as colour, texture and shape. The three techniques are used for feature extraction such as colour moment, gray level co- occurrence matrix and edge histogram descriptor. To reduce curse of dimensionality and find best optimal features from feature set using feature selection based on genetic algorithm. These features are divided into similar image classes using clustering for fast retrieval and improve the execution time. Clustering technique is done by k-means algorithm. The experimental result shows feature selection using GA reduces the time for retrieval and also increases the retrieval precision, thus it gives better and faster results as compared to normal image retrieval system. The result also shows precision and recall of proposed approach compared to previous approach for each image class. The CBIR system is more efficient and better performs using feature selection based on Genetic Algorithm.

  13. The Genetic Algorithm: A Robust Method for Stress Inversion

    Science.gov (United States)

    Thakur, P.; Srivastava, D. C.; Gupta, P. K.

    2016-12-01

    The knowledge of stress states in Earth`s crust is a fundamental objective in many tectonic, seismological and engineering geological studies. Geologists and geophysicists routinely practice methods for determination of the stress tensor from inversion of observations on the stress indicators, such as faults, earthquakes and calcite twin lamellae. While the stress inversion is essentially a nonlinear problem, it is commonly solved by linearization, under some assumptions, in most existing methods. These algorithms not only oversimplify the problem but are also vulnerable to entrapment of the solution in a local optimum. We propose a nonlinear heuristic technique, the genetic algorithm method, that searches the global optimum without making any linearizing assumption or simplification. The method mimics the natural evolutionary process of selection, crossover, mutation, and minimises the composite misfit function for searching the global optimum, the fittest stress tensor. The validity of the method is successfully tested on synthetic fault-slip observations in different tectonic settings and also in situations where the observations contain noisy data. These results are compared with those obtained from the other common methods. The genetic algorithm method is superior to other common methods, in particular, in the oblique tectonic settings where none of the principal stresses is directed vertically.

  14. An Improved Chaos Genetic Algorithm for T-Shaped MIMO Radar Antenna Array Optimization

    Directory of Open Access Journals (Sweden)

    Xin Fu

    2014-01-01

    Full Text Available In view of the fact that the traditional genetic algorithm easily falls into local optimum in the late iterations, an improved chaos genetic algorithm employed chaos theory and genetic algorithm is presented to optimize the low side-lobe for T-shaped MIMO radar antenna array. The novel two-dimension Cat chaotic map has been put forward to produce its initial population, improving the diversity of individuals. The improved Tent map is presented for groups of individuals of a generation with chaos disturbance. Improved chaotic genetic algorithm optimization model is established. The algorithm presented in this paper not only improved the search precision, but also avoids effectively the problem of local convergence and prematurity. For MIMO radar, the improved chaos genetic algorithm proposed in this paper obtains lower side-lobe level through optimizing the exciting current amplitude. Simulation results show that the algorithm is feasible and effective. Its performance is superior to the traditional genetic algorithm.

  15. Optimization in optical systems revisited: Beyond genetic algorithms

    Science.gov (United States)

    Gagnon, Denis; Dumont, Joey; Dubé, Louis

    2013-05-01

    Designing integrated photonic devices such as waveguides, beam-splitters and beam-shapers often requires optimization of a cost function over a large solution space. Metaheuristics - algorithms based on empirical rules for exploring the solution space - are specifically tailored to those problems. One of the most widely used metaheuristics is the standard genetic algorithm (SGA), based on the evolution of a population of candidate solutions. However, the stochastic nature of the SGA sometimes prevents access to the optimal solution. Our goal is to show that a parallel tabu search (PTS) algorithm is more suited to optimization problems in general, and to photonics in particular. PTS is based on several search processes using a pool of diversified initial solutions. To assess the performance of both algorithms (SGA and PTS), we consider an integrated photonics design problem, the generation of arbitrary beam profiles using a two-dimensional waveguide-based dielectric structure. The authors acknowledge financial support from the Natural Sciences and Engineering Research Council of Canada (NSERC).

  16. Protein complexes predictions within protein interaction networks using genetic algorithms.

    Science.gov (United States)

    Ramadan, Emad; Naef, Ahmed; Ahmed, Moataz

    2016-07-25

    Protein-protein interaction networks are receiving increased attention due to their importance in understanding life at the cellular level. A major challenge in systems biology is to understand the modular structure of such biological networks. Although clustering techniques have been proposed for clustering protein-protein interaction networks, those techniques suffer from some drawbacks. The application of earlier clustering techniques to protein-protein interaction networks in order to predict protein complexes within the networks does not yield good results due to the small-world and power-law properties of these networks. In this paper, we construct a new clustering algorithm for predicting protein complexes through the use of genetic algorithms. We design an objective function for exclusive clustering and overlapping clustering. We assess the quality of our proposed clustering algorithm using two gold-standard data sets. Our algorithm can identify protein complexes that are significantly enriched in the gold-standard data sets. Furthermore, our method surpasses three competing methods: MCL, ClusterOne, and MCODE in terms of the quality of the predicted complexes. The source code and accompanying examples are freely available at http://faculty.kfupm.edu.sa/ics/eramadan/GACluster.zip .

  17. Marine Traffic Optimization Using Petri Net and Genetic Algorithm

    Directory of Open Access Journals (Sweden)

    Anita Gudelj

    2012-11-01

    Full Text Available The paper deals with the traffic control and job optimization in the marine canal system. The moving of vessels can be described as a set of discrete events and states. Some of these states can be undesirable such as conflicts and deadlocks. It is necessary to apply adequate control policy to avoid deadlocks and blocks the vessels’ moving only in the case of dangerous situation. This paper addresses the use of Petri net as modelling and scheduling tool in this context. To find better solutions the authors propose the integration of Petri net with a genetic algorithm. Also, a matrix based formal method is proposed for analyzing discrete event dynamic system (DEDS. The algorithm is developed to deal with multi-project, multi-constrained scheduling problem with shared resources. It is verified by a computer simulation using MATLAB environment.

  18. Development of an adaptive genetic algorithm for simulation optimization

    Directory of Open Access Journals (Sweden)

    Rafael de Carvalho Miranda

    2015-07-01

    Full Text Available Optimization methods in discrete-event simulation have become widespread in numerous applications. However, the methods´ performance falls sharply in terms of computational time when more than one decision variable is handled. Current assay develops an adaptive genetic algorithm for the simulation optimization capable of achieving satisfactory results in time efficiency and response quality when compared to optimization software packages on the market. A series of experiments was elaborated to define the algorithm’s most significant parameters and to propose adaptations. According to the results, the most significant parameters are population size and number of generations. Further, adaptive strategies were proposed for these parameters which enabled the algorithm to obtain good results in response quality and time necessary to converge when compared to a commercial software package.

  19. A New Adaptive Hungarian Mating Scheme in Genetic Algorithms

    Directory of Open Access Journals (Sweden)

    Chanju Jung

    2016-01-01

    Full Text Available In genetic algorithms, selection or mating scheme is one of the important operations. In this paper, we suggest an adaptive mating scheme using previously suggested Hungarian mating schemes. Hungarian mating schemes consist of maximizing the sum of mating distances, minimizing the sum, and random matching. We propose an algorithm to elect one of these Hungarian mating schemes. Every mated pair of solutions has to vote for the next generation mating scheme. The distance between parents and the distance between parent and offspring are considered when they vote. Well-known combinatorial optimization problems, the traveling salesperson problem, and the graph bisection problem are used for the test bed of our method. Our adaptive strategy showed better results than not only pure and previous hybrid schemes but also existing distance-based mating schemes.

  20. Genetic algorithms for optimal design and control of adaptive structures

    CERN Document Server

    Ribeiro, R; Dias-Rodrigues, J; Vaz, M

    2000-01-01

    Future High Energy Physics experiments require the use of light and stable structures to support their most precise radiation detection elements. These large structures must be light, highly stable, stiff and radiation tolerant in an environment where external vibrations, high radiation levels, material aging, temperature and humidity gradients are not negligible. Unforeseen factors and the unknown result of the coupling of environmental conditions, together with external vibrations, may affect the position stability of the detectors and their support structures compromising their physics performance. Careful optimization of static and dynamic behavior must be an essential part of the engineering design. Genetic Algorithms ( GA) belong to the group of probabilistic algorithms, combining elements of direct and stochastic search. They are more robust than existing directed search methods with the advantage of maintaining a population of potential solutions. There is a class of optimization problems for which Ge...

  1. Application of genetic algorithm for scheduling and schedule coordination problems

    Energy Technology Data Exchange (ETDEWEB)

    Shrivastava, P. [S. P. Engineering College, Dept. of Civil Engineering, Andheri (W), Mumbai (India); Dhingra, S. L. [ITT Bombay, Dept. of Civil Enginering, Mumbai (India); Gundaliya, P. J. [L. D. College of Engineeering, Dept. of Civil Engineering, Ahmedabad, Gujrat (India)

    2002-12-31

    Problems of scheduling and schedule co-ordination and the conflicting objectives of transportation system users and transportation system operators are discussed. In schedule coordination problems time plays an important role. Users are interested in coordinating services within acceptable waiting time, whereas operators prefer to have lesser service and want to meet higher demands, which invariably increases waiting time. The problems are complex and conventional approaches to solving them are not useful. Genetic algorithm has been identified as an approach which performs well in solving such multi-objective problems. The general algorithmic approach has been found to be highly sensitive to penalties, but assuming that computational time is no constraint and realistic penalties can be decided in advance, judged in terms of their relative importance to outcomes, results can improve substantially. 13 refs., 3 tabs.

  2. Optimal Design of a Hydrogen Community by Genetic Algorithms

    Energy Technology Data Exchange (ETDEWEB)

    Rodolfo Dufo Lopeza; Jose Luis Bernal Agustin [Department of Electrical Engineering - University of Zaragoza, Marya de Luna Street 3, Zaragoza (Spain); Luis Correas Uson; Ismael Aso Aguarta [Foundation for the Development of New Hydrogen Technologies in Aragon, Walqa Tecnologic Park, Huesca (Spain)

    2006-07-01

    A study was conducted for the implementation of two Hydrogen Communities, following the recommendations of the HY-COM initiative of the European Commission. The proposed communities find their place in the municipality of Sabinanigo (Aragon, Spain). Two cases are analyzed, one off-grid village house near Sabinanigo, and a house situated in the town proper. The study was carried out with the HOGA program, Hybrid Optimization by Genetic Algorithms. A description is provided for the algorithms. The off-grid study deals with a hybrid PV-wind system with hydrogen storage for AC supply to an isolated house. The urban study is related to hydrogen production by means of hybrid renewable sources available locally (photovoltaic, wind and hydro). These complement the existing industrial electrolysis processes, in order to cater for the energy requirements of a small fleet of municipal hydrogen-powered vehicles. HOGA was used to optimize both hybrid systems. Dimensioning and deployment estimations are also provided. (authors)

  3. Optimal Design of a Hydrogen Community by Genetic Algorithms

    Energy Technology Data Exchange (ETDEWEB)

    Rodolfo Dufo Lopez; Jose Luis Bernal Agustin [Department of Electrical Engineering - University of Zaragoza, Maria de Luna Street 3, Zaragoza (Spain); Luis Correas Uson; Ismael Aso Aguarta [Foundation for the Development of New Hydrogen Technologies in Aragon. Walqa Tecnologic Park, Huesca (Spain)

    2006-07-01

    A study was conducted for the implementation of two Hydrogen Communities, following the recommendations of the HY-COM initiative of the European Commission. The proposed communities find their place in the municipality of Sabinanigo (Aragon, Spain). Two cases are analyzed, one off-grid village house near Sabinanigo, and a house situated in the town proper. The study was carried out with the HOGA program, Hybrid Optimization by Genetic Algorithms. A description is provided for the algorithms. The off-grid study deals with a hybrid pv-wind system with hydrogen storage for AC supply to an isolated house. The urban study is related to hydrogen production by means of hybrid renewable sources available locally (photovoltaic, wind and hydro). These complement the existing industrial electrolysis processes, in order to cater for the energy requirements of a small fleet of municipal hydrogen-powered vehicles. HOGA was used to optimize both hybrid systems. Dimensioning and deployment estimations are also provided. (authors)

  4. Diversity Controlling Genetic Algorithm for Order Acceptance and Scheduling Problem

    Directory of Open Access Journals (Sweden)

    Cheng Chen

    2014-01-01

    Full Text Available Selection and scheduling are an important topic in production systems. To tackle the order acceptance and scheduling problem on a single machine with release dates, tardiness penalty, and sequence-dependent setup times, in this paper a diversity controlling genetic algorithm (DCGA is proposed, in which a diversified population is maintained during the whole search process through survival selection considering both the fitness and the diversity of individuals. To measure the similarity between individuals, a modified Hamming distance without considering the unaccepted orders in the chromosome is adopted. The proposed DCGA was validated on 1500 benchmark instances with up to 100 orders. Compared with the state-of-the-art algorithms, the experimental results show that DCGA improves the solution quality obtained significantly, in terms of the deviation from upper bound.

  5. Genetic Algorithm for Multiple Bus Line Coordination on Urban Arterial

    Directory of Open Access Journals (Sweden)

    Zhen Yang

    2015-01-01

    Full Text Available Bus travel time on road section is defined and analyzed with the effect of multiple bus lines. An analytical model is formulated to calculate the total red time a bus encounters when travelling along the arterial. Genetic algorithm is used to optimize the offset scheme of traffic signals to minimize the total red time that all bus lines encounter in two directions of the arterial. The model and algorithm are applied to the major part of Zhongshan North Street in the city of Nanjing. The results show that the methods in this paper can reduce total red time of all the bus lines by 31.9% on the object arterial and thus improve the traffic efficiency of the whole arterial and promote public transport priority.

  6. Optimal location of a mine facility by genetic algorithms

    Energy Technology Data Exchange (ETDEWEB)

    Kumral, M. [Inonu University, Malatya (Turkey). School of Engineering

    2004-06-01

    When raw material is extracted from multiple mines or faces and when the construction of a mine facility is being planned, the selection for the optimal location of the facility is an important consideration because of high transportation costs. This paper presents an approach for the selection of optimal location of a mining facility based on the genetic algorithms which is a directed random search technique. The technique, simple and easy to apply, is demonstrated for the case of the location of a coal washery plant, whose supply comes from five mines with varying reserves and coal qualities, topographical conditions and mine status. The result shows that the solution produced by the proposed algorithm can be used to find the optimal location of any mining facility.

  7. Application of Genetic Algorithms for Parameter Estimation in Liquid Chromatography

    Directory of Open Access Journals (Sweden)

    Orestes Llanes Santiago

    2011-11-01

    Full Text Available Normal 0 21 false false false ES X-NONE X-NONE /* Style Definitions */ table.MsoNormalTable {mso-style-name:"Tabla normal"; mso-tstyle-rowband-size:0; mso-tstyle-colband-size:0; mso-style-noshow:yes; mso-style-priority:99; mso-style-qformat:yes; mso-style-parent:""; mso-padding-alt:0cm 5.4pt 0cm 5.4pt; mso-para-margin:0cm; mso-para-margin-bottom:.0001pt; mso-pagination:widow-orphan; font-size:11.0pt; font-family:"Calibri","sans-serif"; mso-ascii-font-family:Calibri; mso-ascii-theme-font:minor-latin; mso-fareast-font-family:"Times New Roman"; mso-fareast-theme-font:minor-fareast; mso-hansi-font-family:Calibri; mso-hansi-theme-font:minor-latin; mso-bidi-font-family:"Times New Roman"; mso-bidi-theme-font:minor-bidi;} In chromatography, complex inverse problems related to the parameters estimation and process optimization are presented. Metaheuristics methods are known as general purpose approximated algorithms which seek and hopefully find good solutions at a reasonable computational cost. These methods are iterative process to perform a robust search of a solution space. Genetic algorithms are optimization techniques based on the principles of genetics and natural selection. They have demonstrated very good performance as global optimizers in many types of applications, including inverse problems. In this work, the effectiveness of genetic algorithms is investigated to estimate parameters in liquid chromatography.

  8. District Heating Network Design and Configuration Optimization with Genetic Algorithm

    DEFF Research Database (Denmark)

    Li, Hongwei; Svendsen, Svend

    2011-01-01

    the heating plant location is allowed to vary. The connection between the heat generation plant and the end users can be represented with mixed integer and the pipe friction and heat loss formulations are non-linear. In order to find the optimal DH distribution pipeline configuration, the genetic algorithm...... which handles the mixed integer nonlinear programming problem was chosen. The network configuration was represented through binary and integer encoding and was optimized in terms of the net present cost (NPC). The optimization results indicated that the optimal DH network configuration is determined...

  9. Approximate Quantum Adders with Genetic Algorithms: An IBM Quantum Experience

    Directory of Open Access Journals (Sweden)

    Li Rui

    2017-07-01

    Full Text Available It has been proven that quantum adders are forbidden by the laws of quantum mechanics. We analyze theoretical proposals for the implementation of approximate quantum adders and optimize them by means of genetic algorithms, improving previous protocols in terms of efficiency and fidelity. Furthermore, we experimentally realize a suitable approximate quantum adder with the cloud quantum computing facilities provided by IBM Quantum Experience. The development of approximate quantum adders enhances the toolbox of quantum information protocols, paving the way for novel applications in quantum technologies.

  10. Genetic algorithms for dipole location of fetal magnetocardiography.

    Science.gov (United States)

    Escalona-Vargas, D; Murphy, P; Lowery, C L; Eswaran, H

    2016-08-01

    In this paper, we explore the use of Maximum Likelihood (ML) method with Genetic Algorithms (GA) as global optimization procedure for source reconstruction in fetal magnetocardiography (fMCG) data. A multiple equivalent current dipole (ECD) model was used for sources active in different time samples. Inverse solutions across time were obtained for a single-dipole approximation to estimate the trajectory of the dipole position. We compared the GA and SIMPLEX methods in a simulation environment under noise conditions. Methods are applied on a real fMCG data. Results show robust estimators of the cardiac sources when GA is used as optimization technique.

  11. GARCON - Genetic Algorithm for Rectangular Cuts OptimizatioN.

    CERN Document Server

    Drozdetskiy, A

    2007-01-01

    We will present Genetic Algorithm for Rectangular Cuts OptimizatioN (GARCON) program and demonstrate its functionality on a simple HEP analysis example. The program automatically performs rectangular cuts optimization and verification for stability in a multi-dimensional cuts phase space. The program has been successfully used by a number of different analyses presented in the Compact Muon Solenoid (CMS collaboration) Physics Technical Design Report (Large Hadron Collider (LHC), CERN, Geneva, Switzerland), corresponding results are also published in a number of papers in 2006.

  12. Asset management using genetic algorithm: Evidence from Tehran Stock Exchange

    Directory of Open Access Journals (Sweden)

    Abbas Sarijaloo

    2014-02-01

    Full Text Available This paper presents an empirical investigation to study the effect of market management using Markowitz theorem. The study uses the information of 50 best performers on Tehran Stock Exchange over the period 2006-2009 and, using Markowitz theorem, the efficient asset allocation are determined and the result are analyzed. The proposed model of this paper has been solved using genetic algorithm. The results indicate that Tehran Stock Exchange has managed to perform much better than average world market in most years of studies especially on year 2009. The results of our investigation have also indicated that one could reach outstanding results using GA and forming efficient portfolio.

  13. A versatile multi-objective FLUKA optimization using Genetic Algorithms

    Science.gov (United States)

    Vlachoudis, Vasilis; Antoniucci, Guido Arnau; Mathot, Serge; Kozlowska, Wioletta Sandra; Vretenar, Maurizio

    2017-09-01

    Quite often Monte Carlo simulation studies require a multi phase-space optimization, a complicated task, heavily relying on the operator experience and judgment. Examples of such calculations are shielding calculations with stringent conditions in the cost, in residual dose, material properties and space available, or in the medical field optimizing the dose delivered to a patient under a hadron treatment. The present paper describes our implementation inside flair[1] the advanced user interface of FLUKA[2,3] of a multi-objective Genetic Algorithm[Erreur ! Source du renvoi introuvable.] to facilitate the search for the optimum solution.

  14. Application of genetic algorithms in nonlinear heat conduction problems.

    Science.gov (United States)

    Kadri, Muhammad Bilal; Khan, Waqar A

    2014-01-01

    Genetic algorithms are employed to optimize dimensionless temperature in nonlinear heat conduction problems. Three common geometries are selected for the analysis and the concept of minimum entropy generation is used to determine the optimum temperatures under the same constraints. The thermal conductivity is assumed to vary linearly with temperature while internal heat generation is assumed to be uniform. The dimensionless governing equations are obtained for each selected geometry and the dimensionless temperature distributions are obtained using MATLAB. It is observed that GA gives the minimum dimensionless temperature in each selected geometry.

  15. [Genetic algorithm for fermentation kinetics of submerged fermentation by Morchella].

    Science.gov (United States)

    Wang, Ying; Piao, Meizi; Sun, Yonghai

    2008-08-01

    Fermentation kinetics is important for optimizing control and up-scaling fermentation process. We studied submerged fermentation kinetics of Morchella. Applying the genetic Algorithm in the Matlab software platform, we compared suitability of the Monod and Logistic models, both are commonly used in process of fungal growth, to describe Morchella growth kinetics. Meanwhile, we evaluated parameters involved in the models for Morchella growth, EPS production and substrate consumption. The results indicated that Logistic model fit better with the experimental data. The average error of this model was 5.8%. This kinetics model can be useful for optimizing and up-scaling fungal fermentation process.

  16. Load Balancing in Multi Cloud Computing Environment with Genetic Algorithm

    Science.gov (United States)

    Vhansure, Fularani; Deshmukh, Apurva; Sumathy, S.

    2017-11-01

    Cloud is a pool of resources that is available on pay per use model. It provides services to the user which is increasing rapidly. Load balancing is an issue because it cannot handle so many requests at a time. It is also known as NP complete problem. In traditional system the functions consist of various parameter values to maximise it in order to achieve best optimal individualsolutions. Challenge is when there are many parameters of solutionsin the system space. Another challenge is to optimize the function which is much more complex. In this paper, various techniques to handle load balancing virtually (VM) as well as physically (nodes) using genetic algorithm is discussed.

  17. Performance of genetic algorithms in search for water splitting perovskites

    DEFF Research Database (Denmark)

    Jain, A.; Castelli, Ivano Eligio; Hautier, G.

    2013-01-01

    , the performance of the GA can be further improved to approximately 12–17 better than random search. We discuss the effect of population size, selection function, crossover function, mutation rate, fitness function, and elitism on the final result, finding that selection function and elitism are especially......We examine the performance of genetic algorithms (GAs) in uncovering solar water light splitters over a space of almost 19,000 perovskite materials. The entire search space was previously calculated using density functional theory to determine solutions that fulfill constraints on stability, band...

  18. Dynamic Crossover and Mutation Genetic Algorithm Based on Expansion Sampling

    Science.gov (United States)

    Dong, Min; Wu, Yan

    The traditional genetic algorithm gets in local optimum easily, and its convergence rate is not satisfactory. So this paper proposed an improvement, using dynamic cross and mutation rate cooperate with expansion sampling to solve these two problems. The expansion sampling means the new individuals must compete with the old generation when create new generation, as a result, the excellent half ones are selected into the next generation. Whereafter several experiments were performed to compare the proposed method with some other improvements. The results are satisfactory. The experiment results show that the proposed method is better than other improvements at both precision and convergence rate.

  19. Random search optimization based on genetic algorithm and discriminant function

    Science.gov (United States)

    Kiciman, M. O.; Akgul, M.; Erarslanoglu, G.

    1990-01-01

    The general problem of optimization with arbitrary merit and constraint functions, which could be convex, concave, monotonic, or non-monotonic, is treated using stochastic methods. To improve the efficiency of the random search methods, a genetic algorithm for the search phase and a discriminant function for the constraint-control phase were utilized. The validity of the technique is demonstrated by comparing the results to published test problem results. Numerical experimentation indicated that for cases where a quick near optimum solution is desired, a general, user-friendly optimization code can be developed without serious penalties in both total computer time and accuracy.

  20. Introduction to the application of genetic algorithms in engineering

    Directory of Open Access Journals (Sweden)

    I. S. Shaw

    1998-07-01

    Full Text Available Genetic algorithms constitute a new research area in the field of artificial intelligence. This work is aimed at their application in specific areas of engineering where good results have already been achieved. The purpose of this work is to provide a basic introduction for students as well as experienced engineers who wish to upgrade their knowledge. A distinctive feature of artificial intelligence is that instead of mathematical models, either direct human experience or certain functions of the human brain for the modelling of physical phenomena are used.

  1. Optimisation of underground mine decline development system using genetic algorithm

    Directory of Open Access Journals (Sweden)

    Gligorić Zoran

    2014-01-01

    Full Text Available When deposit is composed of few ore bodies it is necessary to interconnect them into one integrated system. Suppose the deposit characteristics indicate that decline development system is preferred one. In such environment we treat development of an underground mine as access infrastructure composed of different decline sections. Access infrastructure designing can be treated as spanning the spatial network which will connect all main terminals (points. In our model we defined spatial network by adequate nonlinear constrained objective function representing the cost of mine development and ore haulage. To find the minimum value of the objective function we use Genetic algorithm.

  2. A Hybrid Genetic Algorithm for the Multiple Crossdocks Problem

    Directory of Open Access Journals (Sweden)

    Zhaowei Miao

    2012-01-01

    Full Text Available We study a multiple crossdocks problem with supplier and customer time windows, where any violation of time windows will incur a penalty cost and the flows through the crossdock are constrained by fixed transportation schedules and crossdock capacities. We prove this problem to be NP-hard in the strong sense and therefore focus on developing efficient heuristics. Based on the problem structure, we propose a hybrid genetic algorithm (HGA integrating greedy technique and variable neighborhood search method to solve the problem. Extensive experiments under different scenarios were conducted, and results show that HGA outperforms CPLEX solver, providing solutions in realistic timescales.

  3. Approximate Quantum Adders with Genetic Algorithms: An IBM Quantum Experience

    Science.gov (United States)

    Li, Rui; Alvarez-Rodriguez, Unai; Lamata, Lucas; Solano, Enrique

    2017-07-01

    It has been proven that quantum adders are forbidden by the laws of quantum mechanics. We analyze theoretical proposals for the implementation of approximate quantum adders and optimize them by means of genetic algorithms, improving previous protocols in terms of efficiency and fidelity. Furthermore, we experimentally realize a suitable approximate quantum adder with the cloud quantum computing facilities provided by IBM Quantum Experience. The development of approximate quantum adders enhances the toolbox of quantum information protocols, paving the way for novel applications in quantum technologies.

  4. A New Approach to Tuning Heuristic Parameters of Genetic Algorithms

    Czech Academy of Sciences Publication Activity Database

    Holeňa, Martin

    2006-01-01

    Roč. 3, č. 3 (2006), s. 562-569 ISSN 1790-0832. [AIKED'06. WSEAS International Conference on Artificial Intelligence , Knowledge Engineering and Data Bases. Madrid, 15.02.2006-17.02.2006] R&D Projects: GA ČR(CZ) GA201/05/0325; GA ČR(CZ) GA201/05/0557 Institutional research plan: CEZ:AV0Z10300504 Keywords : evolutionary optimization * genetic algorithms * heuristic parameters * parameter tuning * artificial neural networks * convergence speed * population diversity Subject RIV: IN - Informatics, Computer Science

  5. A versatile multi-objective FLUKA optimization using Genetic Algorithms

    Directory of Open Access Journals (Sweden)

    Vlachoudis Vasilis

    2017-01-01

    Full Text Available Quite often Monte Carlo simulation studies require a multi phase-space optimization, a complicated task, heavily relying on the operator experience and judgment. Examples of such calculations are shielding calculations with stringent conditions in the cost, in residual dose, material properties and space available, or in the medical field optimizing the dose delivered to a patient under a hadron treatment. The present paper describes our implementation inside flair[1] the advanced user interface of FLUKA[2,3] of a multi-objective Genetic Algorithm[Erreur ! Source du renvoi introuvable.] to facilitate the search for the optimum solution.

  6. Mass optimization of engineering structures applying genetic algorithms

    Directory of Open Access Journals (Sweden)

    Valentina Gerfolveden

    2015-07-01

    Full Text Available The paper proposes a technology for mass optimization of two-dimensional body applying genetic algorithms. Main attention is focused on geometry of 2D body, i. e. search for optimal coordinates of body points. Direct analysis of 2D body – von Mises stress determination – is performed using original program based on finite element method. The set of design parameters contains the coordinates of body points in 2D space. The results of numerical experiments proved the proposed technology to be efficient tool for solution of 2D body mass optimization problem.DOI: 10.15181/csat.v2i2.892

  7. Full design of fuzzy controllers using genetic algorithms

    Science.gov (United States)

    Homaifar, Abdollah; Mccormick, ED

    1992-01-01

    This paper examines the applicability of genetic algorithms (GA) in the complete design of fuzzy logic controllers. While GA has been used before in the development of rule sets or high performance membership functions, the interdependence between these two components dictates that they should be designed together simultaneously. GA is fully capable of creating complete fuzzy controllers given the equations of motion of the system, eliminating the need for human input in the design loop. We show the application of this new method to the development of a cart controller.

  8. Application of Genetic Algorithms in Nonlinear Heat Conduction Problems

    Directory of Open Access Journals (Sweden)

    Muhammad Bilal Kadri

    2014-01-01

    Full Text Available Genetic algorithms are employed to optimize dimensionless temperature in nonlinear heat conduction problems. Three common geometries are selected for the analysis and the concept of minimum entropy generation is used to determine the optimum temperatures under the same constraints. The thermal conductivity is assumed to vary linearly with temperature while internal heat generation is assumed to be uniform. The dimensionless governing equations are obtained for each selected geometry and the dimensionless temperature distributions are obtained using MATLAB. It is observed that GA gives the minimum dimensionless temperature in each selected geometry.

  9. Genetic Algorithms for the Optimal Design of Superconducting Accelerator Magnets

    CERN Document Server

    Ramberger, S

    1998-01-01

    The paper describes the use of genetic algorithms with the concept of niching for the optimal design of superconducting magnets for the Large Hadron Collider, LHC at CERN. The method provides the designer with a number of local optima which can be further examined with respect to objectives such as ease of coil winding, sensitivity to manufacturing tolerances and local electromagnetic force distribution. A 6 block dipole coil was found to have advantages compared to the standard 5 block version which was previously designed using deterministic optimization methods. Results were proven by a short model magnet recently built and tested at CERN.

  10. REKONFIGURASI JARING DISTRIBUSI TENAGA LISTRIK MENGGUNAKAN BREEDER GENETIC ALGORITHM (BGA

    Directory of Open Access Journals (Sweden)

    Cok. Gede Indra Partha

    2009-05-01

    Full Text Available Kebutuhan akan daya listrik saat ini semakin meningkat, seiring dengan perkembangan teknologi, cara hidup, kebutuhan dan budaya di daerah tersebut. Untuk itu keandalan dan kontinuitas pelayanan, sistem transmisidan distribusi perlu ditingkatkan untuk memperoleh pelayanan yang optimal dengan losses terendah. Padapenelitian ini digunakan metode Breeder Algoritma Genetika (BGA yang telah dikembangkan dalam optimasibeban seimbang untuk rekonfigurasi jaring distribusi tegangan menengah (JTM. Proses optimasi beban dilakukandengan cara merubah switch-switch pada penyulang (sebagai gen-gen dalam kromosom jaring distribusi sehinggadiperoleh jaring distribusi yang paling optimal. Hasil analisis menggunakan BGA menunjukkan konfigurasi baruyang optimal dengan losses terendah serta lebih cepat konvergen jika dibandingkan dengan Genetic Algorithm(GA biasa.

  11. Multi-objective Optimization on Helium Liquefier Using Genetic Algorithm

    Science.gov (United States)

    Wang, H. R.; Xiong, L. Y.; Peng, N.; Meng, Y. R.; Liu, L. Q.

    2017-02-01

    Research on optimization of helium liquefier is limited at home and abroad, and most of the optimization is single-objective based on Collins cycle. In this paper, a multi-objective optimization is conducted using genetic algorithm (GA) on the 40 L/h helium liquefier developed by Technical Institute of Physics and Chemistry of the Chinese Academy of Science (TIPC, CAS), steady solutions are obtained in the end. In addition, the exergy loss of the optimized system is studied in the case of with and without liquid nitrogen pre-cooling. The results have guiding significance for the future design of large helium liquefier.

  12. Inverse problem study of isolated wing by genetic algorithm

    Energy Technology Data Exchange (ETDEWEB)

    Zhang, F.; Chen, S.; Khalid, M. [National Research Council (NRC), Inst. for Aerospace Research (IAR), Ottawa, Ontario (Canada)]. E-mail: Steve.Zhang@nrc-cnrc.gc.ca

    2004-07-01

    The Genetic Algorithm (GA), coupled with the potential flow solver KTRAN, was successfully applied to redesigning the ONERA M6 wing. A 6th order B-spline curve was used to represent each section of the wing. The actual values of the (x, y) coordinates of the control nodes for the B-spline curves were designated as the design variables. The least squares difference between the actual and target pressures was used as the objective function. The NACA0012 airfoil section was taken as an initial geometry. Both pressure distributions and geometry of the redesigned wing agreed well with their target values, demonstrating the ability of this technique. (author)

  13. Improved Adaptive LSB Steganography Based on Chaos and Genetic Algorithm

    Directory of Open Access Journals (Sweden)

    Yu Lifang

    2010-01-01

    Full Text Available We propose a novel steganographic method in JPEG images with high performance. Firstly, we propose improved adaptive LSB steganography, which can achieve high capacity while preserving the first-order statistics. Secondly, in order to minimize visual degradation of the stego image, we shuffle bits-order of the message based on chaos whose parameters are selected by the genetic algorithm. Shuffling message's bits-order provides us with a new way to improve the performance of steganography. Experimental results show that our method outperforms classical steganographic methods in image quality, while preserving characteristics of histogram and providing high capacity.

  14. Recognition of digital characteristics based new improved genetic algorithm

    Science.gov (United States)

    Wang, Meng; Xu, Guoqiang; Lin, Zihao

    2017-08-01

    In the field of digital signal processing, Estimating the characteristics of signal modulation parameters is an significant research direction. The paper determines the set of eigenvalue which can show the difference of the digital signal modulation based on the deep research of the new improved genetic algorithm. Firstly take them as the best gene pool; secondly, The best gene pool will be changed in the genetic evolvement by selecting, overlapping and eliminating each other; Finally, Adapting the strategy of futher enhance competition and punishment to more optimizer the gene pool and ensure each generation are of high quality gene. The simulation results show that this method not only has the global convergence, stability and faster convergence speed.

  15. GPGPU Implementation of a Genetic Algorithm for Stereo Refinement

    Directory of Open Access Journals (Sweden)

    Álvaro Arranz

    2015-03-01

    Full Text Available During the last decade, the general-purpose computing on graphics processing units Graphics (GPGPU has turned out to be a useful tool for speeding up many scientific calculations. Computer vision is known to be one of the fields with more penetration of these new techniques. This paper explores the advantages of using GPGPU implementation to speedup a genetic algorithm used for stereo refinement. The main contribution of this paper is analyzing which genetic operators take advantage of a parallel approach and the description of an efficient state- of-the-art implementation for each one. As a result, speed-ups close to x80 can be achieved, demonstrating to be the only way of achieving close to real-time performance.

  16. Optimisation of assembly scheduling in VCIM systems using genetic algorithm

    Science.gov (United States)

    Dao, Son Duy; Abhary, Kazem; Marian, Romeo

    2017-01-01

    Assembly plays an important role in any production system as it constitutes a significant portion of the lead time and cost of a product. Virtual computer-integrated manufacturing (VCIM) system is a modern production system being conceptually developed to extend the application of traditional computer-integrated manufacturing (CIM) system to global level. Assembly scheduling in VCIM systems is quite different from one in traditional production systems because of the difference in the working principles of the two systems. In this article, the assembly scheduling problem in VCIM systems is modeled and then an integrated approach based on genetic algorithm (GA) is proposed to search for a global optimised solution to the problem. Because of dynamic nature of the scheduling problem, a novel GA with unique chromosome representation and modified genetic operations is developed herein. Robustness of the proposed approach is verified by a numerical example.

  17. Evolving neural networks using a genetic algorithm for heartbeat classification.

    Science.gov (United States)

    Sekkal, Mansouria; Chikh, Mohamed Amine; Settouti, Nesma

    2011-07-01

    This study investigates the effectiveness of a genetic algorithm (GA) evolved neural network (NN) classifier and its application to the classification of premature ventricular contraction (PVC) beats. As there is no standard procedure to determine the network structure for complicated cases, generally the design of the NN would be dependent on the user's experience. To prevent this problem, we propose a neural classifier that uses a GA for the determination of optimal connections between neurons for better recognition. The MIT-BIH arrhythmia database is employed to evaluate its accuracy. First, the topology of the NN was determined using the trial and error method. Second, the genetic operators were carefully designed to optimize the neural network structure. Performance and accuracy of the two techniques are presented and compared. Copyright © 2011 Informa UK, Ltd.

  18. Blink reflex role in algorithmic genetic testing of inherited polyneuropathies.

    Science.gov (United States)

    Wang, Wei; Litchy, William J; Mandrekar, Jay; Dyck, Peter J; Klein, Christopher J

    2017-03-01

    In severely affected inherited polyneuropathy patients, primary demyelination can be difficult to determine by routine extremity limb nerve conduction studies (NCS). Blink reflexes may help classify severe polyneuropathies as either axonal or demyelinating. However, blink reflex studies have not been studied systematically in any genetically confirmed cohort. Patients with a genetic diagnosis who had undergone blink reflex testing and extremity NCS were identified retrospectively. Blink reflex R1 latency, extremity NCS, and severity were compared. We identified 26 demyelinating and 23 axonal, genetically confirmed cases, including 20 with PMP22 duplications. In 12 (25%), the ulnar CMAP amplitude was ≤0.5 mV making electrophysiological classification difficult. However, the R1-latency cutoff of >13 ms (demyelinating) robustly classified all patients regardless of severity. We show that blink reflex studies are reliable for identification of inherited demyelinating polyneuropathy regardless of severity and can facilitate algorithmic decisions in genetic testing. Muscle Nerve 55: 316-322, 2017. © 2016 Wiley Periodicals, Inc.

  19. Internal lattice reconfiguration for diversity tuning in Cellular Genetic Algorithms.

    Science.gov (United States)

    Morales-Reyes, Alicia; Erdogan, Ahmet T

    2012-01-01

    Cellular Genetic Algorithms (cGAs) have attracted the attention of researchers due to their high performance, ease of implementation and massive parallelism. Maintaining an adequate balance between exploitative and explorative search is essential when studying evolutionary optimization techniques. In this respect, cGAs inherently possess a number of structural configuration parameters that are able to sustain diversity during evolution. In this study, the internal reconfiguration of the lattice is proposed to constantly or adaptively control the exploration-exploitation trade-off. Genetic operators are characterized in their simplest form since algorithmic performance is assessed on implemented reconfiguration mechanisms. Moreover, internal reconfiguration allows the adjacency of individuals to be maintained. Hence, any improvement in performance is only a consequence of topological changes. Two local selection methods presenting opposite selection pressures are used in order to evaluate the influence of the proposed techniques. Problems ranging from continuous to real world and combinatorial are tackled. Empirical results are supported statistically in terms of efficiency and efficacy.

  20. Partial AUC maximization for essential gene prediction using genetic algorithms.

    Science.gov (United States)

    Hwang, Kyu-Baek; Ha, Beom-Yong; Ju, Sanghun; Kim, Sangsoo

    2013-01-01

    Identifying genes indispensable for an organism's life and their characteristics is one of the central questions in current biological research, and hence it would be helpful to develop computational approaches towards the prediction of essential genes. The performance of a predictor is usually measured by the area under the receiver operating characteristic curve (AUC). We propose a novel method by implementing genetic algorithms to maximize the partial AUC that is restricted to a specific interval of lower false positive rate (FPR), the region relevant to follow-up experimental validation. Our predictor uses various features based on sequence information, protein-protein interaction network topology, and gene expression profiles. A feature selection wrapper was developed to alleviate the over-fitting problem and to weigh each feature's relevance to prediction. We evaluated our method using the proteome of budding yeast. Our implementation of genetic algorithms maximizing the partial AUC below 0.05 or 0.10 of FPR outperformed other popular classification methods.

  1. Genetic algorithms for geophysical parameter inversion from altimeter data

    Science.gov (United States)

    Ramillien, Guillaume

    2001-11-01

    A new approach for inverting several geophysical parameters at the same time from altimeter and marine data by implementing genetic algorithms (GAs) is presented. These original techniques of optimization based on non-deterministic rules simulate the evolution of a population of candidate solutions for a given objective function to minimize. They offer a robust and efficient alternative to gradient techniques for non-linear parameter inversion. Here genetic algorithms are used for solving a discrete gravity problem of data associated with an undersea relief, to retrieve seven parameters at the same time: the elastic thickness, the mean ocean depth, the seamount location (longitude/latitude), its amplitude, radius and density from its observed gravity/geoid signature. This approach was also successfully used to adjust lithosphere parameters in the real case of the Rarotonga seamount [21.2°S 159.8°W] in the Southern Cook Islands region, where GA simulations provided robust estimates of these seven parameters. The GA found very realistic values for the mean ocean depth and the seamount amplitude and the precise geographical location of Rarotonga Island. Moreover, the values of elastic thickness (~14-15km) and seamount density (~2850-2870kgm-3) estimated by the GA are consistent with the ones proposed in earlier studies.

  2. Ternary alloy material prediction using genetic algorithm and cluster expansion

    Energy Technology Data Exchange (ETDEWEB)

    Chen, Chong [Iowa State Univ., Ames, IA (United States)

    2015-12-01

    This thesis summarizes our study on the crystal structures prediction of Fe-V-Si system using genetic algorithm and cluster expansion. Our goal is to explore and look for new stable compounds. We started from the current ten known experimental phases, and calculated formation energies of those compounds using density functional theory (DFT) package, namely, VASP. The convex hull was generated based on the DFT calculations of the experimental known phases. Then we did random search on some metal rich (Fe and V) compositions and found that the lowest energy structures were body centered cube (bcc) underlying lattice, under which we did our computational systematic searches using genetic algorithm and cluster expansion. Among hundreds of the searched compositions, thirteen were selected and DFT formation energies were obtained by VASP. The stability checking of those thirteen compounds was done in reference to the experimental convex hull. We found that the composition, 24-8-16, i.e., Fe3VSi2 is a new stable phase and it can be very inspiring to the future experiments.

  3. Time-Delay System Identification Using Genetic Algorithm

    DEFF Research Database (Denmark)

    Yang, Zhenyu; Seested, Glen Thane

    2013-01-01

    Due to the unknown dead-time coefficient, the time-delay system identification turns to be a non-convex optimization problem. This paper investigates the identification of a simple time-delay system, named First-Order-Plus-Dead-Time (FOPDT), by using the Genetic Algorithm (GA) technique. The qual......Due to the unknown dead-time coefficient, the time-delay system identification turns to be a non-convex optimization problem. This paper investigates the identification of a simple time-delay system, named First-Order-Plus-Dead-Time (FOPDT), by using the Genetic Algorithm (GA) technique....... The quality and performance of the GA-based identification are compared with those based on extended Least-Mean-Square (LMS) methods, subject to the consideration of different types of time-delay systems, excitation signals, Signal-to-Noise Ratios, and different evaluation criteria. The obtained results...... exhibit that the GA technique has a very promising capability in handling this type of non-convex system identification problem....

  4. Segmentation of thermographic images of hands using a genetic algorithm

    Science.gov (United States)

    Ghosh, Payel; Mitchell, Melanie; Gold, Judith

    2010-01-01

    This paper presents a new technique for segmenting thermographic images using a genetic algorithm (GA). The individuals of the GA also known as chromosomes consist of a sequence of parameters of a level set function. Each chromosome represents a unique segmenting contour. An initial population of segmenting contours is generated based on the learned variation of the level set parameters from training images. Each segmenting contour (an individual) is evaluated for its fitness based on the texture of the region it encloses. The fittest individuals are allowed to propagate to future generations of the GA run using selection, crossover and mutation. The dataset consists of thermographic images of hands of patients suffering from upper extremity musculo-skeletal disorders (UEMSD). Thermographic images are acquired to study the skin temperature as a surrogate for the amount of blood flow in the hands of these patients. Since entire hands are not visible on these images, segmentation of the outline of the hands on these images is typically performed by a human. In this paper several different methods have been tried for segmenting thermographic images: Gabor-wavelet-based texture segmentation method, the level set method of segmentation and our GA which we termed LSGA because it combines level sets with genetic algorithms. The results show a comparative evaluation of the segmentation performed by all the methods. We conclude that LSGA successfully segments entire hands on images in which hands are only partially visible.

  5. Light-weight telescope structure optimized by genetic algorithm

    Science.gov (United States)

    Kurita, Mikio; Ohmori, Hiroshi; Kunda, Masashi; Kawamura, Hiroaki; Noda, Noriaki; Seki, Takayuki; Nishimura, Yuji; Yoshida, Michitoshi; Sato, Shuji; Nagata, Tetsuya

    2010-07-01

    We designed the optics supporting structure (OSS) of a 3.8 m segmented mirror telescope by applying genetic algorithm optimization. The telescope is the first segmented mirror telescope in Japan whose primary mirror consists of 18 petal shaped segment mirrors. The whole mirror is supported by 54 actuators (3 actuators per each segment). In order to realize light-weight and stiff telescope structure, we have adopted full truss structure as OSS of the telescope. We optimized its design by a newly developed software program incorporating genetic algorithm. The program automatically generates new OSS design step-by-step by optimizing the OSS parameters for realizing both light-weight structure and homologous deformation of the mirror surface supported by the OSS against the telescope elevation change simultaneously. The program successfully generated an ultra light-weight OSS design whose weight is only 8 tons including the optical elements and actuators (4 tons) with an eigen frequency of 9.5 Hz. The deviations of the 54 nodes from their original configuration of the designed OSS are less than 100 μm in the range of elevation angle 20 - 90 degrees. A real model of the OSS is under test construction. We also report the results of static test and deformation performance of this OSS model.

  6. Genetic Algorithm Optimisation of PID Controllers for a Multivariable Process

    Directory of Open Access Journals (Sweden)

    Wael Alharbi

    2017-03-01

    Full Text Available This project is about the design of PID controllers and the improvement of outputs in multivariable processes. The optimisation of PID controller for the Shell oil process is presented in this paper, using Genetic Algorithms (GAs. Genetic Algorithms (GAs are used to automatically tune PID controllers according to given specifications. They use an objective function, which is specially formulated and measures the performance of controller in terms of time-domain bounds on the responses of closed-loop process.A specific objective function is suggested that allows the designer for a single-input, single-output (SISO process to explicitly specify the process performance specifications associated with the given problem in terms of time-domain bounds, then experimentally evaluate the closed-loop responses. This is investigated using a simple two-term parametric PID controller tuning problem. The results are then analysed and compared with those obtained using a number of popular conventional controller tuning methods. The intention is to demonstrate that the proposed objective function is inherently capable of accurately quantifying complex performance specifications in the time domain. This is something that cannot normally be employed in conventional controller design or tuning methods.Finally, the recommended objective function will be used to examine the control problems of Multi-Input-Multi-Output (MIMO processes, and the results will be presented in order to determine the efficiency of the suggested control system.

  7. A genetic algorithm approach to recognition and data mining

    Energy Technology Data Exchange (ETDEWEB)

    Punch, W.F.; Goodman, E.D.; Min, Pei [Michigan State Univ., East Lansing, MI (United States)] [and others

    1996-12-31

    We review here our use of genetic algorithm (GA) and genetic programming (GP) techniques to perform {open_quotes}data mining,{close_quotes} the discovery of particular/important data within large datasets, by finding optimal data classifications using known examples. Our first experiments concentrated on the use of a K-nearest neighbor algorithm in combination with a GA. The GA selected weights for each feature so as to optimize knn classification based on a linear combination of features. This combined GA-knn approach was successfully applied to both generated and real-world data. We later extended this work by substituting a GP for the GA. The GP-knn could not only optimize data classification via linear combinations of features but also determine functional relationships among the features. This allowed for improved performance and new information on important relationships among features. We review the effectiveness of the overall approach on examples from biology and compare the effectiveness of the GA and GP.

  8. Daily Commute Time Prediction Based on Genetic Algorithm

    Directory of Open Access Journals (Sweden)

    Fang Zong

    2012-01-01

    Full Text Available This paper presents a joint discrete-continuous model for activity-travel time allocation by employing the ordered probit model for departure time choice and the hazard model for travel time prediction. Genetic algorithm (GA is employed for optimizing the parameters in the hazard model. The joint model is estimated using data collected in Beijing, 2005. With the developed model, departure and travel times for the daily commute trips are predicted and the influence of sociodemographic variables on activity-travel timing decisions is analyzed. Then the whole time allocation for the typical daily commute activities and trips is derived. The results indicate that the discrete choice model and the continuous model match well in the calculation of activity-travel schedule. The results also show that the genetic algorithm contributes to the optimization and thus the high accuracy of the hazard model. The developed joint discrete-continuous model can be used to predict the agenda of a simple daily activity-travel pattern containing only work, and it provides potential for transportation demand management policy analysis.

  9. Using the Genetic Algorithm to Find Coils for Compact Stellarators

    Science.gov (United States)

    Miner, , Jr.; Valanju, P. M.; Hirshman, S. P.; Brooks, A.; Pomphrey, N.

    1999-11-01

    Stellarators are now optimized by finding the shape of the plasma surface that produces a desired mix of physics properties. The challenge is to find a practical coil set that creates that optimized surface with sufficient accuracy to retain the desired physics properties and still meet engineering and experimental constraints. Given the wide range of possible coil geometries, this is a daunting task requiring iterations between a practical coil geometry and the physics properties produced by it. A novel technique, the Genetic Algorithm (GA) (D.E. Goldberg, Genetic Algorithms in Search, Optimization, and Machine Leaning), (Addison Wesley, New York) 1989., has recently been applied to this problem. The GA is a computational search procedure for finding the global minimum of a target function using natural selection. This technique has been applied to the design of coils for the NCSX. Typically > 30 coil contours are needed to reproduce the necessary accuracy. Using GA, the result can be improved by choosing a small subset (e.g. 10) contours, each carrying different currents from among a much larger number (e.g. 50).

  10. Reliable prediction of adsorption isotherms via genetic algorithm molecular simulation.

    Science.gov (United States)

    LoftiKatooli, L; Shahsavand, A

    2017-01-01

    Conventional molecular simulation techniques such as grand canonical Monte Carlo (GCMC) strictly rely on purely random search inside the simulation box for predicting the adsorption isotherms. This blind search is usually extremely time demanding for providing a faithful approximation of the real isotherm and in some cases may lead to non-optimal solutions. A novel approach is presented in this article which does not use any of the classical steps of the standard GCMC method, such as displacement, insertation, and removal. The new approach is based on the well-known genetic algorithm to find the optimal configuration for adsorption of any adsorbate on a structured adsorbent under prevailing pressure and temperature. The proposed approach considers the molecular simulation problem as a global optimization challenge. A detailed flow chart of our so-called genetic algorithm molecular simulation (GAMS) method is presented, which is entirely different from traditions molecular simulation approaches. Three real case studies (for adsorption of CO2 and H2 over various zeolites) are borrowed from literature to clearly illustrate the superior performances of the proposed method over the standard GCMC technique. For the present method, the average absolute values of percentage errors are around 11% (RHO-H2), 5% (CHA-CO2), and 16% (BEA-CO2), while they were about 70%, 15%, and 40% for the standard GCMC technique, respectively.

  11. DNA Cryptography and Deep Learning using Genetic Algorithm with NW algorithm for Key Generation.

    Science.gov (United States)

    Kalsi, Shruti; Kaur, Harleen; Chang, Victor

    2017-12-05

    Cryptography is not only a science of applying complex mathematics and logic to design strong methods to hide data called as encryption, but also to retrieve the original data back, called decryption. The purpose of cryptography is to transmit a message between a sender and receiver such that an eavesdropper is unable to comprehend it. To accomplish this, not only we need a strong algorithm, but a strong key and a strong concept for encryption and decryption process. We have introduced a concept of DNA Deep Learning Cryptography which is defined as a technique of concealing data in terms of DNA sequence and deep learning. In the cryptographic technique, each alphabet of a letter is converted into a different combination of the four bases, namely; Adenine (A), Cytosine (C), Guanine (G) and Thymine (T), which make up the human deoxyribonucleic acid (DNA). Actual implementations with the DNA don't exceed laboratory level and are expensive. To bring DNA computing on a digital level, easy and effective algorithms are proposed in this paper. In proposed work we have introduced firstly, a method and its implementation for key generation based on the theory of natural selection using Genetic Algorithm with Needleman-Wunsch (NW) algorithm and Secondly, a method for implementation of encryption and decryption based on DNA computing using biological operations Transcription, Translation, DNA Sequencing and Deep Learning.

  12. An Introduction to Genetic Algorithms and to Their Use in Information Retrieval.

    Science.gov (United States)

    Jones, Gareth; And Others

    1994-01-01

    Genetic algorithms, a class of nondeterministic algorithms in which the role of chance makes the precise nature of a solution impossible to guarantee, seem to be well suited to combinatorial-optimization problems in information retrieval. Provides an introduction to techniques and characteristics of genetic algorithms and illustrates their…

  13. Combining neural networks and genetic algorithms for hydrological flow forecasting

    Science.gov (United States)

    Neruda, Roman; Srejber, Jan; Neruda, Martin; Pascenko, Petr

    2010-05-01

    We present a neural network approach to rainfall-runoff modeling for small size river basins based on several time series of hourly measured data. Different neural networks are considered for short time runoff predictions (from one to six hours lead time) based on runoff and rainfall data observed in previous time steps. Correlation analysis shows that runoff data, short time rainfall history, and aggregated API values are the most significant data for the prediction. Neural models of multilayer perceptron and radial basis function networks with different numbers of units are used and compared with more traditional linear time series predictors. Out of possible 48 hours of relevant history of all the input variables, the most important ones are selected by means of input filters created by a genetic algorithm. The genetic algorithm works with population of binary encoded vectors defining input selection patterns. Standard genetic operators of two-point crossover, random bit-flipping mutation, and tournament selection were used. The evaluation of objective function of each individual consists of several rounds of building and testing a particular neural network model. The whole procedure is rather computational exacting (taking hours to days on a desktop PC), thus a high-performance mainframe computer has been used for our experiments. Results based on two years worth data from the Ploucnice river in Northern Bohemia suggest that main problems connected with this approach to modeling are ovetraining that can lead to poor generalization, and relatively small number of extreme events which makes it difficult for a model to predict the amplitude of the event. Thus, experiments with both absolute and relative runoff predictions were carried out. In general it can be concluded that the neural models show about 5 per cent improvement in terms of efficiency coefficient over liner models. Multilayer perceptrons with one hidden layer trained by back propagation algorithm and

  14. GENETIC ALGORITHM APPLICATION FOR MULTI-CRITERIA SCHEDULING PROBLEM

    Directory of Open Access Journals (Sweden)

    I. V. Arkhipov

    2015-05-01

    Full Text Available The paper describes mathematical model and method of task solution for defining an enterprise work performance schedule. Two-stage feedstock processing is supposed to exist at the enterprise. At the first stage the sawing process into semimanufactured products is done. The second stage (finished products manufacturing stage includes durable processing of obtained semimanufactured products at one of the interchangeable work centers. The sawing process into semimanufactured products is carried out according to a plan, developed in advance and in compliance with technological charts. Scheduling consists of separate cutting calculation and planning of all feedstock cutting with the aim of the most effective specification task performance based on available reserves. Following the cutting plan, an enterprise work performance schedule is created. This schedule consists of the cutting sequence with volume, start and end time, and plan for loading of after-treatment. The solution to this problem becomes more involved due to the necessity of taking into account all features, limitations and parameters of process equipment, as well as of raw material and production orders. Special method based on genetic algorithm has been proposed for handling the problem. The algorithm has been tested on several different realproduction plans. Its efficiency estimation is given. The software systemimplemented on the proposed algorithm has been tested on real operating data of several sawmills. Reduction of machine idle time and incomplete production decrease has been confirmed by the enterprise specialists.

  15. Source mask optimization using real-coded genetic algorithms

    Science.gov (United States)

    Yang, Chaoxing; Wang, Xiangzhao; Li, Sikun; Erdmann, Andreas

    2013-04-01

    Source mask optimization (SMO) is considered to be one of the technologies to push conventional 193nm lithography to its ultimate limits. In comparison with other SMO methods that use an inverse problem formulation, SMO based on genetic algorithm (GA) requires very little knowledge of the process, and has the advantage of flexible problem formulation. Recent publications on SMO using a GA employ a binary-coded GA. In general, the performance of a GA depends not only on the merit or fitness function, but also on the parameters, operators and their algorithmic implementation. In this paper, we propose a SMO method using real-coded GA where the source and mask solutions are represented by floating point strings instead of bit strings. Besides from that, the selection, crossover, and mutation operators are replaced by corresponding floating-point versions. Both binary-coded and real-coded genetic algorithms were implemented in two versions of SMO and compared in numerical experiments, where the target patterns are staggered contact holes and a logic pattern with critical dimensions of 100 nm, respectively. The results demonstrate the performance improvement of the real-coded GA in comparison to the binary-coded version. Specifically, these improvements can be seen in a better convergence behavior. For example, the numerical experiments for the logic pattern showed that the average number of generations to converge to a proper fitness of 6.0 using the real-coded method is 61.8% (100 generations) less than that using binary-coded method.

  16. Classification of ETM+ Remote Sensing Image Based on Hybrid Algorithm of Genetic Algorithm and Back Propagation Neural Network

    Directory of Open Access Journals (Sweden)

    Haisheng Song

    2013-01-01

    Full Text Available The back propagation neural network (BPNN algorithm can be used as a supervised classification in the processing of remote sensing image classification. But its defects are obvious: falling into the local minimum value easily, slow convergence speed, and being difficult to determine intermediate hidden layer nodes. Genetic algorithm (GA has the advantages of global optimization and being not easy to fall into local minimum value, but it has the disadvantage of poor local searching capability. This paper uses GA to generate the initial structure of BPNN. Then, the stable, efficient, and fast BP classification network is gotten through making fine adjustments on the improved BP algorithm. Finally, we use the hybrid algorithm to execute classification on remote sensing image and compare it with the improved BP algorithm and traditional maximum likelihood classification (MLC algorithm. Results of experiments show that the hybrid algorithm outperforms improved BP algorithm and MLC algorithm.

  17. Combined clinical and genetic testing algorithm for cervical cancer diagnosis.

    Science.gov (United States)

    Liou, Yu-Ligh; Zhang, Tao-Lan; Yan, Tian; Yeh, Ching-Tung; Kang, Ya-Nan; Cao, Lanqin; Wu, Nayiyuan; Chang, Chi-Feng; Wang, Huei-Jen; Yen, Carolyn; Chu, Tang-Yuan; Zhang, Yi; Zhang, Yu; Zhou, Honghao

    2016-01-01

    Opportunistic screening in hospitals is widely used to effectively reduce the incidence rate of cervical cancer in China and other developing countries. This study aimed to identify clinical risk factor algorithms that combine gynecologic examination and molecular testing (paired box gene 1 (PAX1) or zinc finger protein 582 (ZNF582) methylation or HPV16/18) results to improve diagnostic accuracy. The delta Cp of methylated PAX1 and ZNF582 was obtained via quantitative methylation-specific PCR in a training set (57 CIN2- and 43 cervical intraepithelial neoplasia ≥grade 3 (CIN3+) women), and the individual and combination gene sensitivities and specificities were determined. The detection accuracy of three algorithms combining gynecologic findings and genetic test results was then compared in a randomized case-control study comprising 449 women referred for colposcopic examination by gynecologists in the outpatient department of Xiangya Hospital between November 2011 and March 2013. Significant association was observed between CIN3+ and methylated PAX1 or ZNF582 in combination with HPV16/18 (OR:15.52, 95 % CI:7.73-31.18). The sensitivities and specificities of methylated PAX1 or ZNF582 combined with HPV16/18 for CIN3+ women were 89.2 and 76.0 %, or 85.4 and 80.1 %, respectively. Of the three algorithms applied to cohort data and validated in the study, two indicated 100 % sensitivity in detecting cervical cancer and a low rate of referrals for colposcopy. These algorithms might contribute to precise and objective cervical cancer diagnostics in the outpatient departments of hospitals in countries with high mortality and low screening rates or areas with uneven resource distribution.

  18. Weight optimization of plane truss using genetic algorithm

    Science.gov (United States)

    Neeraja, D.; Kamireddy, Thejesh; Santosh Kumar, Potnuru; Simha Reddy, Vijay

    2017-11-01

    Optimization of structure on basis of weight has many practical benefits in every engineering field. The efficiency is proportionally related to its weight and hence weight optimization gains prime importance. Considering the field of civil engineering, weight optimized structural elements are economical and easier to transport to the site. In this study, genetic optimization algorithm for weight optimization of steel truss considering its shape, size and topology aspects has been developed in MATLAB. Material strength and Buckling stability have been adopted from IS 800-2007 code of construction steel. The constraints considered in the present study are fabrication, basic nodes, displacements, and compatibility. Genetic programming is a natural selection search technique intended to combine good solutions to a problem from many generations to improve the results. All solutions are generated randomly and represented individually by a binary string with similarities of natural chromosomes, and hence it is termed as genetic programming. The outcome of the study is a MATLAB program, which can optimise a steel truss and display the optimised topology along with element shapes, deflections, and stress results.

  19. Nonlinear dynamics optimization with particle swarm and genetic algorithms for SPEAR3 emittance upgrade

    Energy Technology Data Exchange (ETDEWEB)

    Huang, Xiaobiao; Safranek, James

    2014-09-01

    Nonlinear dynamics optimization is carried out for a low emittance upgrade lattice of SPEAR3 in order to improve its dynamic aperture and Touschek lifetime. Two multi-objective optimization algorithms, a genetic algorithm and a particle swarm algorithm, are used for this study. The performance of the two algorithms are compared. The result shows that the particle swarm algorithm converges significantly faster to similar or better solutions than the genetic algorithm and it does not require seeding of good solutions in the initial population. These advantages of the particle swarm algorithm may make it more suitable for many accelerator optimization applications.

  20. Optimization design of diffractive optical elements by genetic local search algorithms

    Science.gov (United States)

    Zhou, Guangya; Zhao, Xiaolin; Wang, Zongguang; Chen, Yi-Xin; Zhang, Mingsheng

    1999-05-01

    In this paper, a novel optimization algorithm, termed genetic local search algorithm (GLSA), that combines a genetic algorithm (GA) with a local search technique is proposed to design DOE's. This hybrid algorithm performs an improved, more goal-oriented search compare to a purely GA. A 1:17 cross pattern fan out grating and a uniform focal plane intensity profile generator are designed to demonstrate the algorithm we proposed. Numerical results proved that the proposed algorithm are highly robust and efficient. High-quality DOE's are achieved by using the algorithms we proposed.

  1. The merits of a parallel genetic algorithm in solving hard optimization problems

    NARCIS (Netherlands)

    van Soest, A.J.; Casius, L.J.R.

    2003-01-01

    A parallel genetic algorithm for optimization is outlined, and its performance on both mathematical and biomechanical optimization problems is compared to a sequential quadratic programming algorithm, a downhill simplex algorithm and a simulated annealing algorithm. When high-dimensional non-smooth

  2. Genetic Algorithm Optimization of Artificial Neural Networks for Hydrological Modelling

    Science.gov (United States)

    Abrahart, R. J.

    2004-05-01

    This paper will consider the case for genetic algorithm optimization in the development of an artificial neural network model. It will provide a methodological evaluation of reported investigations with respect to hydrological forecasting and prediction. The intention in such operations is to develop a superior modelling solution that will be: \\begin{itemize} more accurate in terms of output precision and model estimation skill; more tractable in terms of personal requirements and end-user control; and/or more robust in terms of conceptual and mechanical power with respect to adverse conditions. The genetic algorithm optimization toolbox could be used to perform a number of specific roles or purposes and it is the harmonious and supportive relationship between neural networks and genetic algorithms that will be highlighted and assessed. There are several neural network mechanisms and procedures that could be enhanced and potential benefits are possible at different stages in the design and construction of an operational hydrological model e.g. division of inputs; identification of structure; initialization of connection weights; calibration of connection weights; breeding operations between successful models; and output fusion associated with the development of ensemble solutions. Each set of opportunities will be discussed and evaluated. Two strategic questions will also be considered: [i] should optimization be conducted as a set of small individual procedures or as one large holistic operation; [ii] what specific function or set of weighted vectors should be optimized in a complex software product e.g. timings, volumes, or quintessential hydrological attributes related to the 'problem situation' - that might require the development flood forecasting, drought estimation, or record infilling applications. The paper will conclude with a consideration of hydrological forecasting solutions developed on the combined methodologies of co-operative co-evolution and

  3. Combining classifiers generated by multi-gene genetic programming for protein fold recognition using genetic algorithm.

    Science.gov (United States)

    Bardsiri, Mahshid Khatibi; Eftekhari, Mahdi; Mousavi, Reza

    2015-01-01

    In this study the problem of protein fold recognition, that is a classification task, is solved via a hybrid of evolutionary algorithms namely multi-gene Genetic Programming (GP) and Genetic Algorithm (GA). Our proposed method consists of two main stages and is performed on three datasets taken from the literature. Each dataset contains different feature groups and classes. In the first step, multi-gene GP is used for producing binary classifiers based on various feature groups for each class. Then, different classifiers obtained for each class are combined via weighted voting so that the weights are determined through GA. At the end of the first step, there is a separate binary classifier for each class. In the second stage, the obtained binary classifiers are combined via GA weighting in order to generate the overall classifier. The final obtained classifier is superior to the previous works found in the literature in terms of classification accuracy.

  4. General asymmetric neutral networks and structure design by genetic algorithms: A learning rule for temporal patterns

    Energy Technology Data Exchange (ETDEWEB)

    Bornholdt, S. [Heidelberg Univ., (Germany). Inst., fuer Theoretische Physik; Graudenz, D. [Lawrence Berkeley Lab., CA (United States)

    1993-07-01

    A learning algorithm based on genetic algorithms for asymmetric neural networks with an arbitrary structure is presented. It is suited for the learning of temporal patterns and leads to stable neural networks with feedback.

  5. The simulation method of chemical composition of vermicular graphite iron on the basis of genetic algorithm

    Science.gov (United States)

    Yusupov, L. R.; Klochkova, K. V.; Simonova, L. A.

    2017-09-01

    The paper presents a methodology of modeling the chemical composition of the composite material via genetic algorithm for optimization of the manufacturing process of products. The paper presents algorithms of methods based on intelligent system of vermicular graphite iron design

  6. Intelligent Controller Design for DC Motor Speed Control based on Fuzzy Logic-Genetic Algorithms Optimization

    OpenAIRE

    Allaoua, Boumediene; Laoufi, Abdellah; Gasbaoui, Brahim; Abdelfatah NASRI; Abdessalam ABDERRAHMANI

    2008-01-01

    In this paper, an intelligent controller of the DC (Direct current) Motor drive is designed using fuzzy logic-genetic algorithms optimization. First, a controller is designed according to fuzzy rules such that the systems are fundamentally robust. To obtain the globally optimal values, parameters of the fuzzy controller are improved by genetic algorithms optimization model. Computer MATLAB work space demonstrate that the fuzzy controller associated to the genetic algorithms approach became ve...

  7. Noise reduction in selective computational ghost imaging using genetic algorithm

    Science.gov (United States)

    Zafari, Mohammad; Ahmadi-Kandjani, Sohrab; Kheradmand, Reza

    2017-03-01

    Recently, we have presented a selective computational ghost imaging (SCGI) method as an advanced technique for enhancing the security level of the encrypted ghost images. In this paper, we propose a modified method to improve the ghost image quality reconstructed by SCGI technique. The method is based on background subtraction using genetic algorithm (GA) which eliminates background noise and gives background-free ghost images. Analyzing the universal image quality index by using experimental data proves the advantage of this modification method. In particular, the calculated value of the image quality index for modified SCGI over 4225 realization shows an 11 times improvement with respect to SCGI technique. This improvement is 20 times in comparison to conventional CGI technique.

  8. Some Studies on Forming Optimization with Genetic Algorithm

    Directory of Open Access Journals (Sweden)

    Ganesh Marotrao KAKANDIKAR

    2012-07-01

    Full Text Available Forming is a compression-tension process involving wide spectrum of operations andflow conditions. The result of the process depends on the large number of parameters and theirinterdependence. The selection of various parameters is still based on trial and error methods. In thispaper the authors present a new approach to optimize the geometry parameters of circularcomponents, process parameters such as blank holder pressure and coefficient of friction etc. Theoptimization problem has been formulated with the objective of optimizing the maximum formingload required in Forming. Genetic algorithm is used as a tool for the optimization: to optimize thedrawing load and to optimize the process parameters. A finite element analysis simulation softwareFast Form Advanced is used for the validations of the results after optimization with prior results.

  9. Optimization of transmission system design based on genetic algorithm

    Directory of Open Access Journals (Sweden)

    Xianbing Chen

    2016-05-01

    Full Text Available Transmission system is a crucial precision mechanism for twin-screw chemi-mechanical pulping equipment. The structure of the system designed by traditional method is not optimal because the structure designed by the traditional methods is easy to fall into the local optimum. To achieve the global optimum, this article applies the genetic algorithm which has grown in recent years in the field of structure optimization. The article uses the volume of transmission system as the objective function to optimize the structure designed by traditional method. Compared to the simulation results, the original structure is not optimal, and the optimized structure is tighter and more reasonable. Based on the optimized results, the transmission shafts in the transmission system are designed and checked, and the parameters of the twin screw are selected and calculated. The article provided an effective method to design the structure of transmission system.

  10. GENETIC ALGORITHM BASED CONCEPT DESIGN TO OPTIMIZE NETWORK LOAD BALANCE

    Directory of Open Access Journals (Sweden)

    Ashish Jain

    2012-07-01

    Full Text Available Multiconstraints optimal network load balancing is an NP-hard problem and it is an important part of traffic engineering. In this research we balance the network load using classical method (brute force approach and dynamic programming is used but result shows the limitation of this method but at a certain level we recognized that the optimization of balanced network load with increased number of nodes and demands is intractable using the classical method because the solution set increases exponentially. In such case the optimization techniques like evolutionary techniques can employ for optimizing network load balance. In this paper we analyzed proposed classical algorithm and evolutionary based genetic approach is devise as well as proposed in this paper for optimizing the balance network load.

  11. Storage Management of Hazardous Containers Using the Genetic Algorithm

    Directory of Open Access Journals (Sweden)

    Yachba Khadidja

    2016-12-01

    Full Text Available This work discusses the problem of dangerous containers storage in a container terminal. Container terminal represents an essential intermodal interfaces for global transportation network. Several materials handling possible to move containers at the port to better meet the needs of ships awaiting loading or unloading. Have a good organization of the terminal's container storage area requires a special effort. Containers search times can be considerable and lead to delays causing financial penalties for terminal management operators. An optimal location for a container in a terminal is very important for companies because this operation reduces transportation costs. In this article, we propose an approach for the placement problem of hazardous containers through the description of an optimization model to solve and optimize available storage space. The interest of this work is to develop a computer tool to enable the identification of the best location of a hazardous container using the Genetic Algorithm.

  12. Inverse problem of HIV cell dynamics using Genetic Algorithms

    Science.gov (United States)

    González, J. A.; Guzmán, F. S.

    2017-01-01

    In order to describe the cell dynamics of T-cells in a patient infected with HIV, we use a flavour of Perelson's model. This is a non-linear system of Ordinary Differential Equations that describes the evolution of healthy, latently infected, infected T-cell concentrations and the free viral cells. Different parameters in the equations give different dynamics. Considering the concentration of these types of cells is known for a particular patient, the inverse problem consists in estimating the parameters in the model. We solve this inverse problem using a Genetic Algorithm (GA) that minimizes the error between the solutions of the model and the data from the patient. These errors depend on the parameters of the GA, like mutation rate and population, although a detailed analysis of this dependence will be described elsewhere.

  13. FRACTIONAL ORDER SYSTEM IDENTIFICATION BASED ON GENETIC ALGORITHMS

    Directory of Open Access Journals (Sweden)

    MAZIN Z. OTHMAN

    2013-12-01

    Full Text Available System identification deals with estimating the plant parameters under control using input-output measuring data. Most of practical plants have fractional order dynamic properties which are based on integration and differentiation of noninteger order. In this work the structure and the parameters of fractional order unknown transfer function are estimated using input-output data. Integer order Least Squares identification is used first to confirm the structure (order of the unknown transfer function. Then, Genetic Algorithms (GAs is followed to find the most accurate fractional order estimate that represents the system. Illustrative examples are presented in which fractional order transfer functions are identified in a way that faithfully estimates the dynamics of the unknown plants.

  14. An Intelligent Model for Pairs Trading Using Genetic Algorithms.

    Science.gov (United States)

    Huang, Chien-Feng; Hsu, Chi-Jen; Chen, Chi-Chung; Chang, Bao Rong; Li, Chen-An

    2015-01-01

    Pairs trading is an important and challenging research area in computational finance, in which pairs of stocks are bought and sold in pair combinations for arbitrage opportunities. Traditional methods that solve this set of problems mostly rely on statistical methods such as regression. In contrast to the statistical approaches, recent advances in computational intelligence (CI) are leading to promising opportunities for solving problems in the financial applications more effectively. In this paper, we present a novel methodology for pairs trading using genetic algorithms (GA). Our results showed that the GA-based models are able to significantly outperform the benchmark and our proposed method is capable of generating robust models to tackle the dynamic characteristics in the financial application studied. Based upon the promising results obtained, we expect this GA-based method to advance the research in computational intelligence for finance and provide an effective solution to pairs trading for investment in practice.

  15. An Intelligent Model for Pairs Trading Using Genetic Algorithms

    Directory of Open Access Journals (Sweden)

    Chien-Feng Huang

    2015-01-01

    Full Text Available Pairs trading is an important and challenging research area in computational finance, in which pairs of stocks are bought and sold in pair combinations for arbitrage opportunities. Traditional methods that solve this set of problems mostly rely on statistical methods such as regression. In contrast to the statistical approaches, recent advances in computational intelligence (CI are leading to promising opportunities for solving problems in the financial applications more effectively. In this paper, we present a novel methodology for pairs trading using genetic algorithms (GA. Our results showed that the GA-based models are able to significantly outperform the benchmark and our proposed method is capable of generating robust models to tackle the dynamic characteristics in the financial application studied. Based upon the promising results obtained, we expect this GA-based method to advance the research in computational intelligence for finance and provide an effective solution to pairs trading for investment in practice.

  16. Hierarchical Stochastic Simulation Algorithm for SBML Models of Genetic Circuits

    Directory of Open Access Journals (Sweden)

    Leandro eWatanabe

    2014-11-01

    Full Text Available This paper describes a hierarchical stochastic simulation algorithm which has been implemented within iBioSim, a tool used to model, analyze, and visualize genetic circuits. Many biological analysis tools flatten out hierarchy before simulation, but there are many disadvantages associated with this approach. First, the memory required to represent the model can quickly expand in the process. Second, the flattening process is computationally expensive. Finally, when modeling a dynamic cellular population within iBioSim, inlining the hierarchy of the model is inefficient since models must grow dynamically over time. This paper discusses a new approach to handle hierarchy on the fly to make the tool faster and more memory-efficient. This approach yields significant performance improvements as compared to the former flat analysis method.

  17. Hybrid genetic algorithm to federal government capital budgetting

    Directory of Open Access Journals (Sweden)

    2017-01-01

    Full Text Available Multidimensional Knapsack problem is a NP-hard problem. The problem has been studied extensively in the literature. In this paper we provides a solution to a capital budgeting problem of Federal Government Budget in Nigeria using Hybrid Genetic Algorithms proposed by [32].We use also used the approach of [33] to model federal government budget by dividing the capital project into four groups which includes : The Economic sector (x1 , The Social Service sector (x2 , The Environmental/Regional Development sector (x3, and The Administration sector ( x4. Using MATLAB software for the analysis, it was observed that the optimal solution was optimal solution value is 277.64 billion naira. The result shows that the first and the second sector will be selected (x1,x2 = 1; x3 , x4=0.

  18. Genetic Algorithms and Nucleation in VIH-AIDS transition.

    Science.gov (United States)

    Barranon, Armando

    2003-03-01

    VIH to AIDS transition has been modeled via a genetic algorithm that uses boom-boom principle and where population evolution is simulated with a cellular automaton based on SIR model. VIH to AIDS transition is signed by nucleation of infected cells and low probability of infection are obtained for different mutation rates in agreement with clinical results. A power law is obtained with a critical exponent close to the critical exponent of cubic, spherical percolation, colossal magnetic resonance, Ising Model and liquid-gas phase transition in heavy ion collisions. Computations were carried out at UAM-A Supercomputing Lab and author acknowledges financial support from Division of CBI at UAM-A.

  19. Statistical Design of Genetic Algorithms for Combinatorial Optimization Problems

    Directory of Open Access Journals (Sweden)

    Moslem Shahsavar

    2011-01-01

    Full Text Available Many genetic algorithms (GA have been applied to solve different NP-complete combinatorial optimization problems so far. The striking point of using GA refers to selecting a combination of appropriate patterns in crossover, mutation, and and so forth and fine tuning of some parameters such as crossover probability, mutation probability, and and so forth. One way to design a robust GA is to select an optimal pattern and then to search for its parameter values using a tuning procedure. This paper addresses a methodology to both optimal pattern selection and the tuning phases by taking advantage of design of experiments and response surface methodology. To show the performances of the proposed procedure and demonstrate its applications, it is employed to design a robust GA to solve a project scheduling problem. Through the statistical comparison analyses between the performances of the proposed method and an existing GA, the effectiveness of the methodology is shown.

  20. Access Network Selection Based on Fuzzy Logic and Genetic Algorithms

    Directory of Open Access Journals (Sweden)

    Mohammed Alkhawlani

    2008-01-01

    Full Text Available In the next generation of heterogeneous wireless networks (HWNs, a large number of different radio access technologies (RATs will be integrated into a common network. In this type of networks, selecting the most optimal and promising access network (AN is an important consideration for overall networks stability, resource utilization, user satisfaction, and quality of service (QoS provisioning. This paper proposes a general scheme to solve the access network selection (ANS problem in the HWN. The proposed scheme has been used to present and design a general multicriteria software assistant (SA that can consider the user, operator, and/or the QoS view points. Combined fuzzy logic (FL and genetic algorithms (GAs have been used to give the proposed scheme the required scalability, flexibility, and simplicity. The simulation results show that the proposed scheme and SA have better and more robust performance over the random-based selection.

  1. Scheduling Diet for Diabetes Mellitus Patients using Genetic Algorithm

    Science.gov (United States)

    Syahputra, M. F.; Felicia, V.; Rahmat, R. F.; Budiarto, R.

    2017-01-01

    Diabetes Melitus (DM) is one of metabolic diseases which affects on productivity and lowers the human resources quality. This disease can be controlled by maintaining and regulating balanced and healthy lifestyle especially for daily diet. However, nowadays, there is no system able to help DM patient to get any information of proper diet. Therefore, an approach is required to provide scheduling diet every day in a week with appropriate nutrition for DM patients to help them regulate their daily diet for healing this disease. In this research, we calculate the number of caloric needs using Harris-Benedict equation and propose genetic algorithm for scheduling diet for DM patient. The results show that the greater the number of individuals, the greater the more the possibility of changes in fitness score approaches the best fitness score. Moreover, the greater the created generation, the more the opportunites to obtain best individual with fitness score approaching 0 or equal to 0.

  2. Optimization of Power Coefficient of Wind Turbine Using Genetic Algorithm

    Science.gov (United States)

    Rajakumar, Sappani; Ravindran, Durairaj; Sivakumar, Mahalingam; Venkatachalam, Gopalan; Muthukumar, Shunmugavelu

    2017-04-01

    In the design of a wind turbine, the goal is to attain the highest possible power output under specified atmospheric conditions. The optimization of power coefficient of horizontal axis wind turbine has been carried out by integration of blade element momentum method and genetic algorithm (GA). The design variables considered are wind velocity, angle of attack and tip speed ratio. The objective function is power coefficient of wind turbine. The different combination of design variables are optimized using GA and then the Power coefficient is optimized. The optimized design variables are validated with the experimental results available in the literature. By this optimization work the optimum design variables of wind turbine can be found economically than experimental work. NACA44XX series airfoils are considered for this optimization work.

  3. Waypoint planning with Dubins Curves using Genetic Algorithms

    DEFF Research Database (Denmark)

    Hansen, Karl Damkjær; La Cour-Harbo, Anders

    2016-01-01

    Mission planning for aircraft is often done as waypoint planning. A sequence of waypoints describing the three-dimensional positions that the aircraft must visit. A common approach is to plan the sequence of the waypoints such that the Euclidean distance between them is minimized. When the high......-level waypoint planning is finished, a finer grained planning is executed to obtain a trajectory that the aircraft must follow. When the waypoints in a plan are distributed far apart compared to the turning radius of the aircraft, the two- step planning approach works well, but when the waypoints are closer......, the kinematics of the aircraft ruins the plan. This work describes an approach that uses a genetic algorithm to solve the waypoint planning problem while considering the kinematics of the aircraft in one single step. This approach entails the addition of a heading and target speed along with the position...

  4. a genetic algorithm in a schedule problem with special constraints

    Directory of Open Access Journals (Sweden)

    Carlos Pérez de la Cruz

    2011-07-01

    Full Text Available Ramírez (2001 introduced the generalized robust coloring problem (GRCP, this problem lets solve timetabling problems which considers constraints such as: two events can not be assigned at the same time and there must be at least d days between two events.The GRCP deals with a robust coloring for a given graph with a fixed number of colors, not necessarily the chromatic number and considers the distance between colors as the penalization of complementary edges. It was shown that the problem is NP-complete, so it is necessary to use approximate methods to find good solutions in a reasonable time. This paper presents a hybrid of a genetic algorithm with a local search for cases of 30-120 hours per week; it is shown that for some cases the found solution is optimal and in other cases the solutions are very promising.

  5. Integer programming model for optimizing bus timetable using genetic algorithm

    Science.gov (United States)

    Wihartiko, F. D.; Buono, A.; Silalahi, B. P.

    2017-01-01

    Bus timetable gave an information for passengers to ensure the availability of bus services. Timetable optimal condition happened when bus trips frequency could adapt and suit with passenger demand. In the peak time, the number of bus trips would be larger than the off-peak time. If the number of bus trips were more frequent than the optimal condition, it would make a high operating cost for bus operator. Conversely, if the number of trip was less than optimal condition, it would make a bad quality service for passengers. In this paper, the bus timetabling problem would be solved by integer programming model with modified genetic algorithm. Modification was placed in the chromosomes design, initial population recovery technique, chromosomes reconstruction and chromosomes extermination on specific generation. The result of this model gave the optimal solution with accuracy 99.1%.

  6. Design of dual-band reflectarray using genetic algorithm

    Science.gov (United States)

    Maruyama, Tamami

    2017-07-01

    This paper proposes novel design method of dual-band reflectarray using genetic algorithm (GA). Ordinary, each elements of reflectarray are designed to have desired reflection phase. However, when we adopt same polarization in dual frequencies, the element configuration designed to satisfy desired reflection phase in one frequency influences the characteristics in other frequency. Therefore, it is difficult to achieve dual-band reflectarray. To address the issues, we adopt two layer patches for element to increase flexibility of design and optimize the patches configuration using GA. As a result, we achieve novel reflectarray that reflect wave towards the direction of theta equal to 27 deg. and phi equal to 0 deg. in dual frequency simultaneously when incidence wave is coming from the direction of theta equal to 0 deg. and phi equal to 0 deg. in dual frequency.

  7. Multidisciplinary optimization of a stiffened shell by genetic algorithm

    Energy Technology Data Exchange (ETDEWEB)

    Mehrabani, Mahdi Maghsoudi; Jafari, Ali Asghar [K.N. Toosi University of Technology, Tehran (Iran, Islamic Republic of); Azadi, Mohammad [Sharif University of Technology, Tehran (Iran, Islamic Republic of)

    2012-02-15

    Vibration analysis of simply supported rotating cross-ply laminated stiffened cylindrical shell is performed using an energy approach which includes variational and averaging method. The stiffeners include rings and stringers. The equations are obtained by Rayleigh-Ritz method and Sander's relations. To validate the present method, the results are compared to the results available in other literatures. A good adoption is observed in different type of results including isotropic shells, rotating laminated shells, stiffened isotropic shells and stiffened laminated shells. Then, the optimization of parameters due to shell and stiffeners is conducted by genetic algorithm (GA) method under weight and frequency constraints. Stiffener shape, material properties and dimensions are also optimized.

  8. An adaptive genetic algorithm for crystal structure prediction

    Energy Technology Data Exchange (ETDEWEB)

    Wu, Shunqing [Xiamen Univ., (People' s Republic of China); Ames Lab., Ames, IA (United States); Ji, Min [Ames Lab., Ames, IA (United States); Wang, Cai-Zhuang [Ames Lab., Ames, IA (United States); Nguyen, Manh Cuong [Ames Lab., Ames, IA (United States); Zhao, Xin [Ames Lab., Ames, IA (United States); Umemoto, K. [Ames Lab., Ames, IA (United States); Univ. of Minnesota, Minneapolis, MN (United States); Wentzcovitch, R. M. [Univ. of Minnesota, Minneapolis, MN (United States); Ho, Kai-Ming [Ames Lab., Ames, IA (United States)

    2013-12-18

    We present a genetic algorithm (GA) for structural search that combines the speed of structure exploration by classical potentials with the accuracy of density functional theory (DFT) calculations in an adaptive and iterative way. This strategy increases the efficiency of the DFT-based GA by several orders of magnitude. This gain allows a considerable increase in the size and complexity of systems that can be studied by first principles. The performance of the method is illustrated by successful structure identifications of complex binary and ternary intermetallic compounds with 36 and 54 atoms per cell, respectively. The discovery of a multi-TPa Mg-silicate phase with unit cell containing up to 56 atoms is also reported. Such a phase is likely to be an essential component of terrestrial exoplanetary mantles.

  9. Maximizing microbial perchlorate degradation using a genetic algorithm: consortia optimization.

    Science.gov (United States)

    Kucharzyk, Katarzyna H; Soule, Terence; Hess, Thomas F

    2013-09-01

    Microorganisms in consortia perform many tasks more effectively than individual organisms and in addition grow more rapidly and in greater abundance. In this work, experimental datasets were assembled consisting of all possible selected combinations of perchlorate reducing strains of microorganisms and their perchlorate degradation rates were evaluated. A genetic algorithm (GA) methodology was successfully applied to define sets of microbial strains to achieve maximum rates of perchlorate degradation. Over the course of twenty generations of optimization using a GA, we saw a statistically significant 2.06 and 4.08-fold increase in average perchlorate degradation rates by consortia constructed using solely the perchlorate reducing bacteria (PRB) and by consortia consisting of PRB and accompanying organisms that did not degrade perchlorate, respectively. The comparison of kinetic rates constant in two types of microbial consortia additionally showed marked increases.

  10. District Heating Network Design and Configuration Optimization with Genetic Algorithm

    Directory of Open Access Journals (Sweden)

    Hongwei Li

    2013-12-01

    Full Text Available In this paper, the configuration of a district heating network which connects from the heating plant to the end users is optimized. Each end user in the network represents a building block. The connections between the heat generation plant and the end users are represented with mixed integer and the pipe friction and heat loss formulations are non-linear. In order to find the optimal district heating network configuration, genetic algorithm which handles the mixed integer nonlinear programming problem is chosen. The network configuration is represented with binary and integer encoding and is optimized in terms of the net present cost. The optimization results indicates that the optimal DH network configuration is determined by multiple factors such as the consumer heating load, the distance between the heating plant to the consumer, the design criteria regarding the pressure and temperature limitation, as well as the corresponding network heat loss.

  11. Tuning of active vibration controllers for ACTEX by genetic algorithm

    Science.gov (United States)

    Kwak, Moon K.; Denoyer, Keith K.

    1999-06-01

    This paper is concerned with the optimal tuning of digitally programmable analog controllers on the ACTEX-1 smart structures flight experiment. The programmable controllers for each channel include a third order Strain Rate Feedback (SRF) controller, a fifth order SRF controller, a second order Positive Position Feedback (PPF) controller, and a fourth order PPF controller. Optimal manual tuning of several control parameters can be a difficult task even though the closed-loop control characteristics of each controller are well known. Hence, the automatic tuning of individual control parameters using Genetic Algorithms is proposed in this paper. The optimal control parameters of each control law are obtained by imposing a constraint on the closed-loop frequency response functions using the ACTEX mathematical model. The tuned control parameters are then uploaded to the ACTEX electronic control electronics and experiments on the active vibration control are carried out in space. The experimental results on ACTEX will be presented.

  12. Analysis of Shrinkage on Thick Plate Part using Genetic Algorithm

    Directory of Open Access Journals (Sweden)

    Najihah S.N.

    2016-01-01

    Full Text Available Injection moulding is the most widely used processes in manufacturing plastic products. Since the quality of injection improves plastic parts are mostly influenced by process conditions, the method to determine the optimum process conditions becomes the key to improving the part quality. This paper presents a systematic methodology to analyse the shrinkage of the thick plate part during the injection moulding process. Genetic Algorithm (GA method was proposed to optimise the process parameters that would result in optimal solutions of optimisation goals. Using the GA, the shrinkage of the thick plate part was improved by 39.1% in parallel direction and 17.21% in the normal direction of melt flow.

  13. Novel genetic algorithm search procedure for LEED surface structure determination.

    Science.gov (United States)

    Viana, M L; dos Reis, D D; Soares, E A; Van Hove, M A; Moritz, W; de Carvalho, V E

    2014-06-04

    Low Energy Electron Diffraction (LEED) is one of the most powerful experimental techniques for surface structure analysis but until now only a trial-and-error approach has been successful. So far, fitting procedures developed to optimize structural and nonstructural parameters-by minimization of the R-factor-have had a fairly small convergence radius, suitable only for local optimization. However, the identification of the global minimum among the several local minima is essential for complex surface structures. Global optimization methods have been applied to LEED structure determination, but they still require starting from structures that are relatively close to the correct one, in order to find the final structure. For complex systems, the number of trial structures and the resulting computation time increase so rapidly that the task of finding the correct model becomes impractical using the present methodologies. In this work we propose a new search method, based on Genetic Algorithms, which is able to determine the correct structural model starting from completely random structures. This method-called here NGA-LEED for Novel Genetic Algorithm for LEED-utilizes bond lengths and symmetry criteria to select reasonable trial structures before performing LEED calculations. This allows a reduction of the parameter space and, consequently of the calculation time, by several orders of magnitude. A refinement of the parameters by least squares fit of simulated annealing is performed only at some intermediate stages and in the final step. The method was successfully tested for two systems, Ag(1 1 1)(4 × 4)-O and Au(1 1 0)-(1 × 2), both in theory versus theory and in theory versus experiment comparisons. Details of the implementation as well as the results for these two systems are presented.

  14. Melanoma prognostic model using tissue microarrays and genetic algorithms.

    Science.gov (United States)

    Gould Rothberg, Bonnie E; Berger, Aaron J; Molinaro, Annette M; Subtil, Antonio; Krauthammer, Michael O; Camp, Robert L; Bradley, William R; Ariyan, Stephan; Kluger, Harriet M; Rimm, David L

    2009-12-01

    As a result of the questionable risk-to-benefit ratio of adjuvant therapies, stage II melanoma is currently managed by observation because available clinicopathologic parameters cannot identify the 20% to 60% of such patients likely to develop metastatic disease. Here, we propose a multimarker molecular prognostic assay that can help triage patients at increased risk of recurrence. Protein expression for 38 candidates relevant to melanoma oncogenesis was evaluated using the automated quantitative analysis (AQUA) method for immunofluorescence-based immunohistochemistry in formalin-fixed, paraffin-embedded specimens from a cohort of 192 primary melanomas collected during 1959 to 1994. The prognostic assay was built using a genetic algorithm and validated on an independent cohort of 246 serial primary melanomas collected from 1997 to 2004. Multiple iterations of the genetic algorithm yielded a consistent five-marker solution. A favorable prognosis was predicted by ATF2 ln(non-nuclear/nuclear AQUA score ratio) of more than -0.052, p21(WAF1) nuclear compartment AQUA score of more than 12.98, p16(INK4A) ln(non-nuclear/nuclear AQUA score ratio) of < or = -0.083, beta-catenin total AQUA score of more than 38.68, and fibronectin total AQUA score of < or = 57.93. Primary tumors that met at least four of these five conditions were considered a low-risk group, and those that met three or fewer conditions formed a high-risk group (log-rank P < .0001). Multivariable proportional hazards analysis adjusting for clinicopathologic parameters shows that the high-risk group has significantly reduced survival on both the discovery (hazard ratio = 2.84; 95% CI, 1.46 to 5.49; P = .002) and validation (hazard ratio = 2.72; 95% CI, 1.12 to 6.58; P = .027) cohorts. This multimarker prognostic assay, an independent determinant of melanoma survival, might be beneficial in improving the selection of stage II patients for adjuvant therapy.

  15. GAPinch: genetic algorithm toolbox for water pinch technology

    Energy Technology Data Exchange (ETDEWEB)

    Prakotpol, D. [Department of Chemical Engineering, Faculty of Engineering, King Mongkut' s University of Technology Thonburi, Bangkok (Thailand); Srinophakun, T. [Department of Chemical Engineering, Faculty of Engineering, Kasetsart University, Bangkok (Thailand)

    2004-07-01

    Wastewater in the chemical industry can be minimized by water reuse. The optimum water usage network leads to a minimum freshwater consumption and wastewater treatment. Genetic algorithm, an objective of this work, is developed for solving the wastewater minimization problem. The optimization model is formulated for both single and multiple contaminants in the class of mixed integer nonlinear programming (MINLP). In this model, there is a set of equality constraints from mass balance that drives the genetic algorithm from addressing the feasible solution. To elucidate this problem, all variables have to be divided into two groups: independent and dependent variables. The values of independent variables come from randomization, whereas the values of dependent variables come from simultaneous solutions of a set of equality constraints after assigning the values of independent variables. This method is applied to the steps of initialization, crossover and mutation. The developed program is the MATLAB{sup TM} toolbox that consists of 29 m-file. The graphic user interface was created in order to make the program easy and convenient. After a user inputs the process condition of the problem into the blank form of GUI, the program will formulate the optimization model and solve for the solution automatically. Then, the optimum results are returned to the user. The program is tested with a process that contains a certain number of water-using operations in which water is used to remove a fixed content of contaminant. In the single contaminant system, this program and Lingo can find the minimum freshwater consumption, but the solutions are different in the configuration of water usage network. These alternative configurations give a wide-vision on the analysis of the system. In the multiple contaminants system, this program can find the same or better results in some test problems. (author)

  16. Use of genetic algorithm for the selection of EEG features

    Science.gov (United States)

    Asvestas, P.; Korda, A.; Kostopoulos, S.; Karanasiou, I.; Ouzounoglou, A.; Sidiropoulos, K.; Ventouras, E.; Matsopoulos, G.

    2015-09-01

    Genetic Algorithm (GA) is a popular optimization technique that can detect the global optimum of a multivariable function containing several local optima. GA has been widely used in the field of biomedical informatics, especially in the context of designing decision support systems that classify biomedical signals or images into classes of interest. The aim of this paper is to present a methodology, based on GA, for the selection of the optimal subset of features that can be used for the efficient classification of Event Related Potentials (ERPs), which are recorded during the observation of correct or incorrect actions. In our experiment, ERP recordings were acquired from sixteen (16) healthy volunteers who observed correct or incorrect actions of other subjects. The brain electrical activity was recorded at 47 locations on the scalp. The GA was formulated as a combinatorial optimizer for the selection of the combination of electrodes that maximizes the performance of the Fuzzy C Means (FCM) classification algorithm. In particular, during the evolution of the GA, for each candidate combination of electrodes, the well-known (Σ, Φ, Ω) features were calculated and were evaluated by means of the FCM method. The proposed methodology provided a combination of 8 electrodes, with classification accuracy 93.8%. Thus, GA can be the basis for the selection of features that discriminate ERP recordings of observations of correct or incorrect actions.

  17. Genetic Algorithm-Based Identification of Fractional-Order Systems

    Directory of Open Access Journals (Sweden)

    Shengxi Zhou

    2013-05-01

    Full Text Available Fractional calculus has become an increasingly popular tool for modeling the complex behaviors of physical systems from diverse domains. One of the key issues to apply fractional calculus to engineering problems is to achieve the parameter identification of fractional-order systems. A time-domain identification algorithm based on a genetic algorithm (GA is proposed in this paper. The multi-variable parameter identification is converted into a parameter optimization by applying GA to the identification of fractional-order systems. To evaluate the identification accuracy and stability, the time-domain output error considering the condition variation is designed as the fitness function for parameter optimization. The identification process is established under various noise levels and excitation levels. The effects of external excitation and the noise level on the identification accuracy are analyzed in detail. The simulation results show that the proposed method could identify the parameters of both commensurate rate and non-commensurate rate fractional-order systems from the data with noise. It is also observed that excitation signal is an important factor influencing the identification accuracy of fractional-order systems.

  18. Adaptive double chain quantum genetic algorithm for constrained optimization problems

    Directory of Open Access Journals (Sweden)

    Haipeng Kong

    2015-02-01

    Full Text Available Optimization problems are often highly constrained and evolutionary algorithms (EAs are effective methods to tackle this kind of problems. To further improve search efficiency and convergence rate of EAs, this paper presents an adaptive double chain quantum genetic algorithm (ADCQGA for solving constrained optimization problems. ADCQGA makes use of double-individuals to represent solutions that are classified as feasible and infeasible solutions. Fitness (or evaluation functions are defined for both types of solutions. Based on the fitness function, three types of step evolution (SE are defined and utilized for judging evolutionary individuals. An adaptive rotation is proposed and used to facilitate updating individuals in different solutions. To further improve the search capability and convergence rate, ADCQGA utilizes an adaptive evolution process (AEP, adaptive mutation and replacement techniques. ADCQGA was first tested on a widely used benchmark function to illustrate the relationship between initial parameter values and the convergence rate/search capability. Then the proposed ADCQGA is successfully applied to solve other twelve benchmark functions and five well-known constrained engineering design problems. Multi-aircraft cooperative target allocation problem is a typical constrained optimization problem and requires efficient methods to tackle. Finally, ADCQGA is successfully applied to solving the target allocation problem.

  19. Optimization of multiresolution segmentation by using a genetic algorithm

    Science.gov (United States)

    Nikfar, Maryam; Zoej, Mohammad Javad Valadan; Mohammadzadeh, Ali; Mokhtarzade, Mehdi; Navabi, Afshin

    2012-01-01

    Most traditional pixel-based analyses are based on the digital number of each pixel. Whereas images can provide more details such as color, size, shape, and texture, object-oriented processing is more advantageous. Multiresolution segmentation, which was proposed by Baatz and Schäpe, is one of the most powerful segmentation algorithms. On the other hand, meaningful segmentation is the most important issue in object-oriented processing. Currently, meaningful segmentation, which is recommended by Baatz's multiresolution segmentation approach, is a trial-and-error task that is very tedious and time consuming. Therefore, a genetic algorithm (GA) is used for finding optimal parameters of Baatz's multiresolution segmentation approach for three building groups' meaningful segmentation. The optimal parameters are found by GA and its generality has been evaluated on a simulated image as well as some IKONOS and GeoEye image patches. The evaluations show the efficiency of GA for finding optimal multiresolution segmentation parameters for meaningful segmentation of the simulated image and the three groups of building images.

  20. Human emotion detector based on genetic algorithm using lip features

    Science.gov (United States)

    Brown, Terrence; Fetanat, Gholamreza; Homaifar, Abdollah; Tsou, Brian; Mendoza-Schrock, Olga

    2010-04-01

    We predicted human emotion using a Genetic Algorithm (GA) based lip feature extractor from facial images to classify all seven universal emotions of fear, happiness, dislike, surprise, anger, sadness and neutrality. First, we isolated the mouth from the input images using special methods, such as Region of Interest (ROI) acquisition, grayscaling, histogram equalization, filtering, and edge detection. Next, the GA determined the optimal or near optimal ellipse parameters that circumvent and separate the mouth into upper and lower lips. The two ellipses then went through fitness calculation and were followed by training using a database of Japanese women's faces expressing all seven emotions. Finally, our proposed algorithm was tested using a published database consisting of emotions from several persons. The final results were then presented in confusion matrices. Our results showed an accuracy that varies from 20% to 60% for each of the seven emotions. The errors were mainly due to inaccuracies in the classification, and also due to the different expressions in the given emotion database. Detailed analysis of these errors pointed to the limitation of detecting emotion based on the lip features alone. Similar work [1] has been done in the literature for emotion detection in only one person, we have successfully extended our GA based solution to include several subjects.

  1. Multi-objective genetic algorithm for pseudoknotted RNA sequence design

    Directory of Open Access Journals (Sweden)

    Akito eTaneda

    2012-04-01

    Full Text Available RNA inverse folding is a computational technology for designing RNA sequences which fold into a user-specified secondary structure. Although pseudoknots are functionally important motifs in RNA structures, less reports concerning the inverse folding of pseudoknotted RNAs have been done compared to those for pseudoknot-free RNA design. In this paper, we present a new version of our multi-objective genetic algorithm (MOGA, MODENA, which we have previously proposed for pseudoknot-free RNA inverse folding. In the new version of MODENA, (i a new crossover operator is implemented and (ii pseudoknot prediction methods, IPknot and HotKnots, are used to evaluate the designed RNA sequences, allowing us to perform the inverse folding of pseudoknotted RNAs. The new version of MODENA with the new crossover operator was benchmarked with a dataset composed of natural pseudoknotted RNA secondary structures, and we found that MODENA can successfully design more pseudoknotted RNAs compared to the other pseudoknot design algorithm. In addition, a sequence constraint function newly implemented in the new version of MODENA was tested by designing RNA sequences which fold into the pseudoknotted structure of a hepatitis delta virus ribozyme; as a result, we successfully designed eight RNA sequences. The new version of MODENA is downloadable from http://rna.eit.hirosaki-u.ac.jp/modena/.

  2. Multi-objective genetic algorithm for pseudoknotted RNA sequence design.

    Science.gov (United States)

    Taneda, Akito

    2012-01-01

    RNA inverse folding is a computational technology for designing RNA sequences which fold into a user-specified secondary structure. Although pseudoknots are functionally important motifs in RNA structures, less reports concerning the inverse folding of pseudoknotted RNAs have been done compared to those for pseudoknot-free RNA design. In this paper, we present a new version of our multi-objective genetic algorithm (MOGA), MODENA, which we have previously proposed for pseudoknot-free RNA inverse folding. In the new version of MODENA, (i) a new crossover operator is implemented and (ii) pseudoknot prediction methods, IPknot and HotKnots, are used to evaluate the designed RNA sequences, allowing us to perform the inverse folding of pseudoknotted RNAs. The new version of MODENA with the new crossover operator was benchmarked with a dataset composed of natural pseudoknotted RNA secondary structures, and we found that MODENA can successfully design more pseudoknotted RNAs compared to the other pseudoknot design algorithm. In addition, a sequence constraint function newly implemented in the new version of MODENA was tested by designing RNA sequences which fold into the pseudoknotted structure of a hepatitis delta virus ribozyme; as a result, we successfully designed eight RNA sequences. The new version of MODENA is downloadable from http://rna.eit.hirosaki-u.ac.jp/modena/.

  3. Primary chromatic aberration elimination via optimization work with genetic algorithm

    Science.gov (United States)

    Wu, Bo-Wen; Liu, Tung-Kuan; Fang, Yi-Chin; Chou, Jyh-Horng; Tsai, Hsien-Lin; Chang, En-Hao

    2008-09-01

    Chromatic Aberration plays a part in modern optical systems, especially in digitalized and smart optical systems. Much effort has been devoted to eliminating specific chromatic aberration in order to match the demand for advanced digitalized optical products. Basically, the elimination of axial chromatic and lateral color aberration of an optical lens and system depends on the selection of optical glass. According to reports from glass companies all over the world, the number of various newly developed optical glasses in the market exceeds three hundred. However, due to the complexity of a practical optical system, optical designers have so far had difficulty in finding the right solution to eliminate small axial and lateral chromatic aberration except by the Damped Least Squares (DLS) method, which is limited in so far as the DLS method has not yet managed to find a better optical system configuration. In the present research, genetic algorithms are used to replace traditional DLS so as to eliminate axial and lateral chromatic, by combining the theories of geometric optics in Tessar type lenses and a technique involving Binary/Real Encoding, Multiple Dynamic Crossover and Random Gene Mutation to find a much better configuration for optical glasses. By implementing the algorithms outlined in this paper, satisfactory results can be achieved in eliminating axial and lateral color aberration.

  4. SOLUTION OF NONLINEAR PROBLEMS IN WATER RESOURCES SYSTEMS BY GENETIC ALGORITHM

    Directory of Open Access Journals (Sweden)

    Ahmet BAYLAR

    1998-03-01

    Full Text Available Genetic Algorithm methodology is a genetic process treated on computer which is considering evolution process in the nature. The genetic operations takes place within the chromosomes stored in computer memory. By means of various operators, the genetic knowledge in chromosomes change continuously and success of the community progressively increases as a result of these operations. The primary purpose of this study is calculation of nonlinear programming problems in water resources systems by Genetic Algorithm. For this purpose a Genetic Algoritm based optimization program were developed. It can be concluded that the results obtained from the genetic search based method give the precise results.

  5. Support Vector Regression and Genetic Algorithm for HVAC Optimal Operation

    Directory of Open Access Journals (Sweden)

    Ching-Wei Chen

    2016-01-01

    Full Text Available This study covers records of various parameters affecting the power consumption of air-conditioning systems. Using the Support Vector Machine (SVM, the chiller power consumption model, secondary chilled water pump power consumption model, air handling unit fan power consumption model, and air handling unit load model were established. In addition, it was found that R2 of the models all reached 0.998, and the training time was far shorter than that of the neural network. Through genetic programming, a combination of operating parameters with the least power consumption of air conditioning operation was searched. Moreover, the air handling unit load in line with the air conditioning cooling load was predicted. The experimental results show that for the combination of operating parameters with the least power consumption in line with the cooling load obtained through genetic algorithm search, the power consumption of the air conditioning systems under said combination of operating parameters was reduced by 22% compared to the fixed operating parameters, thus indicating significant energy efficiency.

  6. MICRONEEDLE STRUCTURE DESIGN AND OPTIMIZATION USING GENETIC ALGORITHM

    Directory of Open Access Journals (Sweden)

    N. A. ISMAIL

    2015-07-01

    Full Text Available This paper presents a Genetic Algorithm (GA based microneedle design and analysis. GA is an evolutionary optimization technique that mimics the natural biological evolution. The design of microneedle structure considers the shape of microneedle, material used, size of the array, the base of microneedle, the lumen base, the height of microneedle, the height of the lumen, and the height of the drug container or reservoir. The GA is executed in conjunction with ANSYS simulation system to assess the design specifications. The GA uses three operators which are reproduction, crossover and mutation to manipulate the genetic composition of the population. In this research, the microneedle is designed to meet a number of significant specifications such as nodal displacement, strain energy, equivalent stress and flow rate of the fluid / drug that flow through its channel / lumen. A comparison study is conducted to investigate the design of microneedle structure with and without the implementation of GA model. The results showed that GA is able to optimize the design parameters of microneedle and is capable to achieve the required specifications with better performance.

  7. Interpretation of magnetic anomalies using a genetic algorithm

    Science.gov (United States)

    Kaftan, İlknur

    2017-08-01

    A genetic algorithm (GA) is an artificial intelligence method used for optimization. We applied a GA to the inversion of magnetic anomalies over a thick dike. Inversion of nonlinear geophysical problems using a GA has advantages because it does not require model gradients or well-defined initial model parameters. The evolution process consists of selection, crossover, and mutation genetic operators that look for the best fit to the observed data and a solution consisting of plausible compact sources. The efficiency of a GA on both synthetic and real magnetic anomalies of dikes by estimating model parameters, such as depth to the top of the dike ( H), the half-width of the dike ( B), the distance from the origin to the reference point ( D), the dip of the thick dike ( δ), and the susceptibility contrast ( k), has been shown. For the synthetic anomaly case, it has been considered for both noise-free and noisy magnetic data. In the real case, the vertical magnetic anomaly from the Pima copper mine in Arizona, USA, and the vertical magnetic anomaly in the Bayburt-Sarıhan skarn zone in northeastern Turkey have been inverted and interpreted. We compared the estimated parameters with the results of conventional inversion methods used in previous studies. We can conclude that the GA method used in this study is a useful tool for evaluating magnetic anomalies for dike models.

  8. Optimization of genomic selection training populations with a genetic algorithm.

    Science.gov (United States)

    Akdemir, Deniz; Sanchez, Julio I; Jannink, Jean-Luc

    2015-05-06

    In this article, we imagine a breeding scenario with a population of individuals that have been genotyped but not phenotyped. We derived a computationally efficient statistic that uses this genetic information to measure the reliability of genomic estimated breeding values (GEBV) for a given set of individuals (test set) based on a training set of individuals. We used this reliability measure with a genetic algorithm scheme to find an optimized training set from a larger set of candidate individuals. This subset was phenotyped to create the training set that was used in a genomic selection model to estimate GEBV in the test set. Our results show that, compared to a random sample of the same size, the use of a set of individuals selected by our method improved accuracies. We implemented the proposed training selection methodology on four sets of data on Arabidopsis, wheat, rice and maize. This dynamic model building process that takes genotypes of the individuals in the test sample into account while selecting the training individuals improves the performance of genomic selection models.

  9. Optimal Robust Motion Controller Design Using Multiobjective Genetic Algorithm

    Science.gov (United States)

    Svečko, Rajko

    2014-01-01

    This paper describes the use of a multiobjective genetic algorithm for robust motion controller design. Motion controller structure is based on a disturbance observer in an RIC framework. The RIC approach is presented in the form with internal and external feedback loops, in which an internal disturbance rejection controller and an external performance controller must be synthesised. This paper involves novel objectives for robustness and performance assessments for such an approach. Objective functions for the robustness property of RIC are based on simple even polynomials with nonnegativity conditions. Regional pole placement method is presented with the aims of controllers' structures simplification and their additional arbitrary selection. Regional pole placement involves arbitrary selection of central polynomials for both loops, with additional admissible region of the optimized pole location. Polynomial deviation between selected and optimized polynomials is measured with derived performance objective functions. A multiobjective function is composed of different unrelated criteria such as robust stability, controllers' stability, and time-performance indexes of closed loops. The design of controllers and multiobjective optimization procedure involve a set of the objectives, which are optimized simultaneously with a genetic algorithm—differential evolution. PMID:24987749

  10. An Evolvement-based Genetic Algorithm for Computer-aided Molecular Docking

    Science.gov (United States)

    Ling, Kang; Xiaoyu, Zhao; Xi, Chen; Xicheng, Wang

    2010-05-01

    Species dynamics model is introduced into the genetic algorithm to reflect the true state of evolution. An adaptive evolution algorithm is developed. In the algorithm, an adaptive strategy is used to overcome the difficulty of confirming the crossover and mutation probabilities. Small population strategy and optimal strategy ensure the diversity of the populations. Numerical results show that introducing species dynamics model can improve the efficiency of the algorithm. Based on the genetic algorithm, a new molecular docking program is developed. Docking result indicates that the algorithm can effectively solve the molecular docking problem.

  11. Optimization of heterogeneous Bin packing using adaptive genetic algorithm

    Science.gov (United States)

    Sridhar, R.; Chandrasekaran, M.; Sriramya, C.; Page, Tom

    2017-03-01

    This research is concentrates on a very interesting work, the bin packing using hybrid genetic approach. The optimal and feasible packing of goods for transportation and distribution to various locations by satisfying the practical constraints are the key points in this project work. As the number of boxes for packing can not be predicted in advance and the boxes may not be of same category always. It also involves many practical constraints that are why the optimal packing makes much importance to the industries. This work presents a combinational of heuristic Genetic Algorithm (HGA) for solving Three Dimensional (3D) Single container arbitrary sized rectangular prismatic bin packing optimization problem by considering most of the practical constraints facing in logistic industries. This goal was achieved in this research by optimizing the empty volume inside the container using genetic approach. Feasible packing pattern was achieved by satisfying various practical constraints like box orientation, stack priority, container stability, weight constraint, overlapping constraint, shipment placement constraint. 3D bin packing problem consists of ‘n’ number of boxes being to be packed in to a container of standard dimension in such a way to maximize the volume utilization and in-turn profit. Furthermore, Boxes to be packed may be of arbitrary sizes. The user input data are the number of bins, its size, shape, weight, and constraints if any along with standard container dimension. This user input were stored in the database and encoded to string (chromosomes) format which were normally acceptable by GA. GA operators were allowed to act over these encoded strings for finding the best solution.

  12. Efficient Dual Domain Decoding of Linear Block Codes Using Genetic Algorithms

    Directory of Open Access Journals (Sweden)

    Ahmed Azouaoui

    2012-01-01

    Full Text Available A computationally efficient algorithm for decoding block codes is developed using a genetic algorithm (GA. The proposed algorithm uses the dual code in contrast to the existing genetic decoders in the literature that use the code itself. Hence, this new approach reduces the complexity of decoding the codes of high rates. We simulated our algorithm in various transmission channels. The performance of this algorithm is investigated and compared with competitor decoding algorithms including Maini and Shakeel ones. The results show that the proposed algorithm gives large gains over the Chase-2 decoding algorithm and reach the performance of the OSD-3 for some quadratic residue (QR codes. Further, we define a new crossover operator that exploits the domain specific information and compare it with uniform and two point crossover. The complexity of this algorithm is also discussed and compared to other algorithms.

  13. A Comparison of a Standard Genetic Algorithm with a Hybrid Genetic Algorithm Applied to Cell Formation Problem

    Directory of Open Access Journals (Sweden)

    Waqas Javaid

    2014-09-01

    Full Text Available Though there are a number of benefits associated with cellular manufacturing systems, its implementation (identification of part families and corresponding machine groups for real life problems is still a challenging task. To handle the complexity of optimizing multiple objectives and larger size of the problem, most of the researchers in the past two decades or so have focused on developing genetic algorithm (GA based techniques. Recently this trend has shifted from standard GA to hybrid GA (HGA based approaches in the quest for greater effectiveness as far as convergence on to the optimum solution is concerned. In order to prove the point, that HGAs possess better convergence abilities than standard GAs, a methodology, initially based on standard GA and later on hybridized with a local search heuristic (LSH, has been developed during this research. Computational experience shows that HGA maintains its accuracy level with increase in problem size, whereas standard GA looses its effectiveness as the problem size grows.

  14. Optimization of Multiple Traveling Salesman Problem Based on Simulated Annealing Genetic Algorithm

    Directory of Open Access Journals (Sweden)

    Xu Mingji

    2017-01-01

    Full Text Available It is very effective to solve the multi variable optimization problem by using hierarchical genetic algorithm. This thesis analyzes both advantages and disadvantages of hierarchical genetic algorithm and puts forward an improved simulated annealing genetic algorithm. The new algorithm is applied to solve the multiple traveling salesman problem, which can improve the performance of the solution. First, it improves the design of chromosomes hierarchical structure in terms of redundant hierarchical algorithm, and it suggests a suffix design of chromosomes; Second, concerning to some premature problems of genetic algorithm, it proposes a self-identify crossover operator and mutation; Third, when it comes to the problem of weak ability of local search of genetic algorithm, it stretches the fitness by mixing genetic algorithm with simulated annealing algorithm. Forth, it emulates the problems of N traveling salesmen and M cities so as to verify its feasibility. The simulation and calculation shows that this improved algorithm can be quickly converged to a best global solution, which means the algorithm is encouraging in practical uses.

  15. Empirical study of self-configuring genetic programming algorithm performance and behaviour

    Science.gov (United States)

    Semenkin, E.; Semenkina, M.

    2015-01-01

    The behaviour of the self-configuring genetic programming algorithm with a modified uniform crossover operator that implements a selective pressure on the recombination stage, is studied over symbolic programming problems. The operator's probabilistic rates interplay is studied and the role of operator variants on algorithm performance is investigated. Algorithm modifications based on the results of investigations are suggested. The performance improvement of the algorithm is demonstrated by the comparative analysis of suggested algorithms on the benchmark and real world problems.

  16. Efficient Improvement of Silage Additives by Using Genetic Algorithms

    Science.gov (United States)

    Davies, Zoe S.; Gilbert, Richard J.; Merry, Roger J.; Kell, Douglas B.; Theodorou, Michael K.; Griffith, Gareth W.

    2000-01-01

    The enormous variety of substances which may be added to forage in order to manipulate and improve the ensilage process presents an empirical, combinatorial optimization problem of great complexity. To investigate the utility of genetic algorithms for designing effective silage additive combinations, a series of small-scale proof of principle silage experiments were performed with fresh ryegrass. Having established that significant biochemical changes occur over an ensilage period as short as 2 days, we performed a series of experiments in which we used 50 silage additive combinations (prepared by using eight bacterial and other additives, each of which was added at six different levels, including zero [i.e., no additive]). The decrease in pH, the increase in lactate concentration, and the free amino acid concentration were measured after 2 days and used to calculate a “fitness” value that indicated the quality of the silage (compared to a control silage made without additives). This analysis also included a “cost” element to account for different total additive levels. In the initial experiment additive levels were selected randomly, but subsequently a genetic algorithm program was used to suggest new additive combinations based on the fitness values determined in the preceding experiments. The result was very efficient selection for silages in which large decreases in pH and high levels of lactate occurred along with low levels of free amino acids. During the series of five experiments, each of which comprised 50 treatments, there was a steady increase in the amount of lactate that accumulated; the best treatment combination was that used in the last experiment, which produced 4.6 times more lactate than the untreated silage. The additive combinations that were found to yield the highest fitness values in the final (fifth) experiment were assessed to determine a range of biochemical and microbiological quality parameters during full-term silage

  17. Reliability Based Spare Parts Management Using Genetic Algorithm

    Directory of Open Access Journals (Sweden)

    Rahul Upadhyay

    2015-08-01

    Full Text Available Effective and efficient inventory management is the key to the economic sustainability of capital intensive modern industries. Inventory grows exponentially with complexity and size of the equipment fleet. Substantial amount of capital is required for maintaining an inventory and therefore its optimization is beneficial for smooth operation of the project at minimum cost of inventory. The size and hence the cost of the inventory is influenced by a large no of factors. This makes the optimization problem complex. This work presents a model to solve the problem of optimization of spare parts inventory. The novelty of this study lies with the fact that the developed method could tackle not only the artificial test case but also a real-world industrial problem. Various investigators developed several methods and semi-analytical tools for obtaining optimum solutions for this problem. In this study non-traditional optimization tool namely genetic algorithms GA are utilized. Apart from this Coxs regression analysis is also used to incorporate the effect of some environmental factors on the demand of spares. It shows the efficacy of the applicability of non-traditional optimization tool like GA to solve these problems. This research illustrates the proposed model with the analysis of data taken from a fleet of dumper operated in a large surface coal mine. The optimum time schedules so suggested by this GA-based model are found to be cost effective. A sensitivity analysis is also conducted for this industrial problem. Objective function is developed and the factors like the effect of season and production pressure overloading towards financial year-ending is included in the equations. Statistical analysis of the collected operational and performance data were carried out with the help of Easy-Fit Ver-5.5.The analysis gives the shape and scale parameter of theoretical Weibull distribution. The Coxs regression coefficient corresponding to excessive loading

  18. New knowledge-based genetic algorithm for excavator boom structural optimization

    Science.gov (United States)

    Hua, Haiyan; Lin, Shuwen

    2014-03-01

    Due to the insufficiency of utilizing knowledge to guide the complex optimal searching, existing genetic algorithms fail to effectively solve excavator boom structural optimization problem. To improve the optimization efficiency and quality, a new knowledge-based real-coded genetic algorithm is proposed. A dual evolution mechanism combining knowledge evolution with genetic algorithm is established to extract, handle and utilize the shallow and deep implicit constraint knowledge to guide the optimal searching of genetic algorithm circularly. Based on this dual evolution mechanism, knowledge evolution and population evolution can be connected by knowledge influence operators to improve the configurability of knowledge and genetic operators. Then, the new knowledge-based selection operator, crossover operator and mutation operator are proposed to integrate the optimal process knowledge and domain culture to guide the excavator boom structural optimization. Eight kinds of testing algorithms, which include different genetic operators, are taken as examples to solve the structural optimization of a medium-sized excavator boom. By comparing the results of optimization, it is shown that the algorithm including all the new knowledge-based genetic operators can more remarkably improve the evolutionary rate and searching ability than other testing algorithms, which demonstrates the effectiveness of knowledge for guiding optimal searching. The proposed knowledge-based genetic algorithm by combining multi-level knowledge evolution with numerical optimization provides a new effective method for solving the complex engineering optimization problem.

  19. Optimal Sensors and Actuators Placement for Large-Scale Unstable Systems via Restricted Genetic Algorithm

    DEFF Research Database (Denmark)

    Seyyed Sakha, Masoud; Shaker, Hamid Reza

    2017-01-01

    expensive. The computational burden is significant in particular for large-scale systems. In this paper, we develop a new technique for placing sensor and actuator in large-scale systems by using Restricted Genetic Algorithm (RGA). The RGA is a kind of genetic algorithm which is developed specifically...

  20. Application of the genetic algorithm for optimisation of large solar hot water systems

    NARCIS (Netherlands)

    Loomans, M.G.L.C.; Visser, H.

    2002-01-01

    An implementation of the genetic algorithm in a design support tool for (large) solar hot water systems is described. The tool calculates the yield and the costs of solar hot water systems based on technical and financial data of the system components. The genetic algorithm allows for optimisation

  1. A modified genetic algorithm with fuzzy roulette wheel selection for job-shop scheduling problems

    Science.gov (United States)

    Thammano, Arit; Teekeng, Wannaporn

    2015-05-01

    The job-shop scheduling problem is one of the most difficult production planning problems. Since it is in the NP-hard class, a recent trend in solving the job-shop scheduling problem is shifting towards the use of heuristic and metaheuristic algorithms. This paper proposes a novel metaheuristic algorithm, which is a modification of the genetic algorithm. This proposed algorithm introduces two new concepts to the standard genetic algorithm: (1) fuzzy roulette wheel selection and (2) the mutation operation with tabu list. The proposed algorithm has been evaluated and compared with several state-of-the-art algorithms in the literature. The experimental results on 53 JSSPs show that the proposed algorithm is very effective in solving the combinatorial optimization problems. It outperforms all state-of-the-art algorithms on all benchmark problems in terms of the ability to achieve the optimal solution and the computational time.

  2. Investigation for digital speckle correlation method based on improved genetic algorithm

    Science.gov (United States)

    Zhang, Ting; Chen, Haoyuan; Yang, Xiankun; Zheng, Xitao

    2011-10-01

    A digital speckle correlation method (DSCM) to measure surface displacements and strains have been developed. The correlate search algorithm plays an important role in DSCM. The traditional correlate search algorithms depend on initial value selection seriously. For this defect, this paper presents DSCM which uses genetic algorithm (GA) to measure surface deformation. In order to overcome the premature convergence of tradition genetic algorithm, this paper utilizes multi-parent crossover and adaptive mutation probability to perform genetic operation. The simulation demonstrates that improved GA increases the speed and the precision of search. With the application of DSCM in measuring deformation of composite laminates, the test reveals an agreement with the anticipated result.

  3. Optimal Sensor Placement in Bridge Structure Based on Immune Genetic Algorithm

    Directory of Open Access Journals (Sweden)

    Zhen-Rui PENG

    2014-10-01

    Full Text Available For the problem of optimal sensor placement (OSP, this paper introduces immune genetic algorithm (IGA, which combines the advantages of genetic algorithm (GA and immune algorithm (IA, to minimize sensors placed in the structure and to obtain more information of structural characteristics. The OSP mode is formulated and integer coding method is proposed to code an antibody to reduce the computational complexity of affinity. Additionally, taking an arch bridge as an example, the results indicate that the problem can be achieved based on IGA method, and IGA has the ability to guarantee the higher calculation accuracy, compared with genetic algorithm (GA.

  4. Genetic Algorithm for Restricted Maximum k-Satisfiability in the Hopfield Network

    Directory of Open Access Journals (Sweden)

    Mohd Shareduwan Bin Mohd Kasihmuddin

    2016-12-01

    Full Text Available The restricted Maximum k-Satisfiability MAX- kSAT is an enhanced Boolean satisfiability counterpart that has attracted numerous amount of research. Genetic algorithm has been the prominent optimization heuristic algorithm to solve constraint optimization problem. The core motivation of this paper is to introduce Hopfield network incorporated with genetic algorithm in solving MAX-kSAT problem. Genetic algorithm will be integrated with Hopfield network as a single network. The proposed method will be compared with the conventional Hopfield network. The results demonstrate that Hopfield network with genetic algorithm outperforms conventional Hopfield networks. Furthermore, the outcome had provided a solid evidence of the robustness of our proposed algorithms to be used in other satisfiability problem.

  5. Steam condenser optimization using Real-parameter Genetic Algorithm for Prototype Fast Breeder Reactor

    Energy Technology Data Exchange (ETDEWEB)

    Jayalal, M.L., E-mail: jayalal@igcar.gov.in [Indira Gandhi Centre for Atomic Research, Kalpakkam 603102, Tamil Nadu (India); Kumar, L. Satish, E-mail: satish@igcar.gov.in [Indira Gandhi Centre for Atomic Research, Kalpakkam 603102, Tamil Nadu (India); Jehadeesan, R., E-mail: jeha@igcar.gov.in [Indira Gandhi Centre for Atomic Research, Kalpakkam 603102, Tamil Nadu (India); Rajeswari, S., E-mail: raj@igcar.gov.in [Indira Gandhi Centre for Atomic Research, Kalpakkam 603102, Tamil Nadu (India); Satya Murty, S.A.V., E-mail: satya@igcar.gov.in [Indira Gandhi Centre for Atomic Research, Kalpakkam 603102, Tamil Nadu (India); Balasubramaniyan, V.; Chetal, S.C. [Indira Gandhi Centre for Atomic Research, Kalpakkam 603102, Tamil Nadu (India)

    2011-10-15

    Highlights: > We model design optimization of a vital reactor component using Genetic Algorithm. > Real-parameter Genetic Algorithm is used for steam condenser optimization study. > Comparison analysis done with various Genetic Algorithm related mechanisms. > The results obtained are validated with the reference study results. - Abstract: This work explores the use of Real-parameter Genetic Algorithm and analyses its performance in the steam condenser (or Circulating Water System) optimization study of a 500 MW fast breeder nuclear reactor. Choice of optimum design parameters for condenser for a power plant from among a large number of technically viable combination is a complex task. This is primarily due to the conflicting nature of the economic implications of the different system parameters for maximizing the capitalized profit. In order to find the optimum design parameters a Real-parameter Genetic Algorithm model is developed and applied. The results obtained are validated with the reference study results.

  6. A New Method to Improve Performance of Resampling Process in Particles Filter by Genetic Algorithm and Gamma Test Algorithm

    Science.gov (United States)

    Wang, Zhenwu; Hut, Rolf; van de Giesen, Nick

    2017-04-01

    Particle filtering is a nonlinear and non-Gaussian dynamical filtering system. It has found widespread applications in hydrological data assimilation. In order to solve the loss of particle diversity exiting in resampling process of particle filter, this research proposes an improved particle filter algorithm using genetic algorithm optimization and Gamma test. This method combines the genetic algorithm and Gamma test into the resampling procedure of particle filter to improve the adaptability and performance of particle filter in data assimilation. First, the particles are classified to three different groups based on resampling method. The particles with high weight values remain unchanged. Then genetic algorithm is used to cross and variate the rest of the particles. In the process of the optimization, the Gamma test method is applied for monitoring the quality of the new generated particles. When the gamma statistic stays stable, the algorithm will end the optimization and continue to perturb next observations in particle algorithm. The algorithm is illustrated for the three-dimensional Lorenz model and the much more complex 40-dimensional Lorenz model. The results demonstrate this method can keep the diversity of the particles and enhance the performance of the particle filter, leading to the promising conjecture that the method is applicable to realistic hydrological problems.

  7. Genetic algorithm learning in a New Keynesian macroeconomic setup.

    Science.gov (United States)

    Hommes, Cars; Makarewicz, Tomasz; Massaro, Domenico; Smits, Tom

    2017-01-01

    In order to understand heterogeneous behavior amongst agents, empirical data from Learning-to-Forecast (LtF) experiments can be used to construct learning models. This paper follows up on Assenza et al. (2013) by using a Genetic Algorithms (GA) model to replicate the results from their LtF experiment. In this GA model, individuals optimize an adaptive, a trend following and an anchor coefficient in a population of general prediction heuristics. We replicate experimental treatments in a New-Keynesian environment with increasing complexity and use Monte Carlo simulations to investigate how well the model explains the experimental data. We find that the evolutionary learning model is able to replicate the three different types of behavior, i.e. convergence to steady state, stable oscillations and dampened oscillations in the treatments using one GA model. Heterogeneous behavior can thus be explained by an adaptive, anchor and trend extrapolating component and the GA model can be used to explain heterogeneous behavior in LtF experiments with different types of complexity.

  8. Orbit determination by genetic algorithm and application to GEO observation

    Science.gov (United States)

    Hinagawa, Hideaki; Yamaoka, Hitoshi; Hanada, Toshiya

    2014-02-01

    This paper demonstrates an initial orbit determination method that solves the problem by a genetic algorithm using two well-known solutions for the Lambert's problem: universal variable method and Battin method. This paper also suggests an intuitive error evaluation method in terms of rotational angle and orbit shape by separating orbit elements into two groups. As reference orbit, mean orbit elements (original two-lines elements) and osculating orbit elements considering the J2 effect are adopted and compared. Our proposed orbit determination method has been tested with actual optical observations of a geosynchronous spacecraft. It should be noted that this demonstration of the orbit determination is limited to one test case. This observation was conducted during approximately 70 min on 2013/05/15 UT. Our method was compared with the orbit elements propagated by SGP4 using the TLE of the spacecraft. The result indicates that our proposed method had a slightly better performance on estimating orbit shape than Gauss's methods and Escobal's method by 120 km. In addition, the result of the rotational angle is closer to the osculating orbit elements than the mean orbit elements by 0.02°, which supports that the estimated orbit is valid.

  9. Improved interpretation of satellite altimeter data using genetic algorithms

    Science.gov (United States)

    Messa, Kenneth; Lybanon, Matthew

    1992-01-01

    Genetic algorithms (GA) are optimization techniques that are based on the mechanics of evolution and natural selection. They take advantage of the power of cumulative selection, in which successive incremental improvements in a solution structure become the basis for continued development. A GA is an iterative procedure that maintains a 'population' of 'organisms' (candidate solutions). Through successive 'generations' (iterations) the population as a whole improves in simulation of Darwin's 'survival of the fittest'. GA's have been shown to be successful where noise significantly reduces the ability of other search techniques to work effectively. Satellite altimetry provides useful information about oceanographic phenomena. It provides rapid global coverage of the oceans and is not as severely hampered by cloud cover as infrared imagery. Despite these and other benefits, several factors lead to significant difficulty in interpretation. The GA approach to the improved interpretation of satellite data involves the representation of the ocean surface model as a string of parameters or coefficients from the model. The GA searches in parallel, a population of such representations (organisms) to obtain the individual that is best suited to 'survive', that is, the fittest as measured with respect to some 'fitness' function. The fittest organism is the one that best represents the ocean surface model with respect to the altimeter data.

  10. Reactive Power Optimization with SVC & TCSC using Genetic Algorithm

    Directory of Open Access Journals (Sweden)

    Biplab Bhattacharyya

    2014-01-01

    Full Text Available In this paper Genetic Algorithm (GA is used as an evolutionary tecthniques for the optimal placement of flexible AC transmission systems (FACTS devices in an interconnected power system. Here two types of FACTS devices has been discussed nemely, Thyristor Controlled Series Capacitor (TCSC and Static Var Compensator (SVC for the economic operation and to reduce the transmission loss. Reactively loading of the system is taken from base to 200% of base loading and the system performance is observed without and with FACTS devices. Optimal placement of FACTS devices in the system is determined by calculating active and reactive power flow in lines. FACTS devices along with reactive generation of generators and transformer tap setting are used for the power transfer capacity using GA. The proposed approach is applied on IEEE 14 and IEEE 30-bus test systems. Finally the effectiveness of the proposed GA based method of placement of FACTS devices is established by comparing the results with another standard method of optimization like Particle Swarm Optimization (PSO technique.

  11. APPROACH FOR RESOURCE MANAGEMENT IN GRID ENVIRONMENTS USING GENETIC ALGORITHM

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    Seyed Javad Mirabedini

    2015-11-01

    Full Text Available The use of ‘proper energy computation system’ is the best method to investigate the problem of energy deficiency, since energy consumption in computational resources is proportional with the work load in the applied program. Also, the best method to improve the use of resources and decrease in energy consumption is dynamic integration of virtual machines which can be a base for the integration of resources in independent systems through virtualization technology, so that it is possible to use resources and equipment for long time and consequently assuring quicker return of investment. Today, grid computation is a new technology connecting heterogeneous computational resources to each other; thus, this structure operates as an individual and integrated virtual machine. Then, it is possible to implement very complex applied programs requiring high processing capacity and huge amount of input data on this virtual machine. In this regard, the purpose of this study is to present an approach for resource management in grid environments using PSO and Genetic algorithms, and also ants colony to find the location of virtual machines.

  12. Exchange inlet optimization by genetic algorithm for improved RBCC performance

    Science.gov (United States)

    Chorkawy, G.; Etele, J.

    2017-09-01

    A genetic algorithm based on real parameter representation using a variable selection pressure and variable probability of mutation is used to optimize an annular air breathing rocket inlet called the Exchange Inlet. A rapid and accurate design method which provides estimates for air breathing, mixing, and isentropic flow performance is used as the engine of the optimization routine. Comparison to detailed numerical simulations show that the design method yields desired exit Mach numbers to within approximately 1% over 75% of the annular exit area and predicts entrained air massflows to between 1% and 9% of numerically simulated values depending on the flight condition. Optimum designs are shown to be obtained within approximately 8000 fitness function evaluations in a search space on the order of 106. The method is also shown to be able to identify beneficial values for particular alleles when they exist while showing the ability to handle cases where physical and aphysical designs co-exist at particular values of a subset of alleles within a gene. For an air breathing engine based on a hydrogen fuelled rocket an exchange inlet is designed which yields a predicted air entrainment ratio within 95% of the theoretical maximum.

  13. Genetic Algorithm (GA)-Based Inclinometer Layout Optimization.

    Science.gov (United States)

    Liang, Weijie; Zhang, Ping; Chen, Xianping; Cai, Miao; Yang, Daoguo

    2015-04-17

    This paper presents numerical simulation results of an airflow inclinometer with sensitivity studies and thermal optimization of the printed circuit board (PCB) layout for an airflow inclinometer based on a genetic algorithm (GA). Due to the working principle of the gas sensor, the changes of the ambient temperature may cause dramatic voltage drifts of sensors. Therefore, eliminating the influence of the external environment for the airflow is essential for the performance and reliability of an airflow inclinometer. In this paper, the mechanism of an airflow inclinometer and the influence of different ambient temperatures on the sensitivity of the inclinometer will be examined by the ANSYS-FLOTRAN CFD program. The results show that with changes of the ambient temperature on the sensing element, the sensitivity of the airflow inclinometer is inversely proportional to the ambient temperature and decreases when the ambient temperature increases. GA is used to optimize the PCB thermal layout of the inclinometer. The finite-element simulation method (ANSYS) is introduced to simulate and verify the results of our optimal thermal layout, and the results indicate that the optimal PCB layout greatly improves (by more than 50%) the sensitivity of the inclinometer. The study may be useful in the design of PCB layouts that are related to sensitivity improvement of gas sensors.

  14. Ripple-Spreading Network Model Optimization by Genetic Algorithm

    Directory of Open Access Journals (Sweden)

    Xiao-Bing Hu

    2013-01-01

    Full Text Available Small-world and scale-free properties are widely acknowledged in many real-world complex network systems, and many network models have been developed to capture these network properties. The ripple-spreading network model (RSNM is a newly reported complex network model, which is inspired by the natural ripple-spreading phenomenon on clam water surface. The RSNM exhibits good potential for describing both spatial and temporal features in the development of many real-world networks where the influence of a few local events spreads out through nodes and then largely determines the final network topology. However, the relationships between ripple-spreading related parameters (RSRPs of RSNM and small-world and scale-free topologies are not as obvious or straightforward as in many other network models. This paper attempts to apply genetic algorithm (GA to tune the values of RSRPs, so that the RSNM may generate these two most important network topologies. The study demonstrates that, once RSRPs are properly tuned by GA, the RSNM is capable of generating both network topologies and therefore has a great flexibility to study many real-world complex network systems.

  15. Innovative applications of genetic algorithms to problems in accelerator physics

    Directory of Open Access Journals (Sweden)

    Alicia Hofler

    2013-01-01

    Full Text Available The genetic algorithm (GA is a powerful technique that implements the principles nature uses in biological evolution to optimize a multidimensional nonlinear problem. The GA works especially well for problems with a large number of local extrema, where traditional methods (such as conjugate gradient, steepest descent, and others fail or, at best, underperform. The field of accelerator physics, among others, abounds with problems which lend themselves to optimization via GAs. In this paper, we report on the successful application of GAs in several problems related to the existing Continuous Electron Beam Accelerator Facility nuclear physics machine, the proposed Medium-energy Electron-Ion Collider at Jefferson Lab, and a radio frequency gun-based injector. These encouraging results are a step forward in optimizing accelerator design and provide an impetus for application of GAs to other problems in the field. To that end, we discuss the details of the GAs used, include a newly devised enhancement which leads to improved convergence to the optimum, and make recommendations for future GA developments and accelerator applications.

  16. The adaptive analysis of visual cognition using genetic algorithms.

    Science.gov (United States)

    Cook, Robert G; Qadri, Muhammad A J

    2013-10-01

    Two experiments used a novel, open-ended, and adaptive test procedure to examine visual cognition in animals. Using a genetic algorithm, a pigeon was tested repeatedly from a variety of different initial conditions for its solution to an intermediate brightness search task. On each trial, the animal had to accurately locate and peck a target element of intermediate brightness from among a variable number of surrounding darker and lighter distractor elements. Displays were generated from 6 parametric variables, or genes (distractor number, element size, shape, spacing, target brightness, and distractor brightness). Display composition changed over time, or evolved, as a function of the bird's differential accuracy within the population of values for each gene. Testing 3 randomized initial conditions and 1 set of controlled initial conditions, element size and number of distractors were identified as the most important factors controlling search accuracy, with distractor brightness, element shape, and spacing making secondary contributions. The resulting changes in this multidimensional stimulus space suggested the existence of a set of conditions that the bird repeatedly converged upon regardless of initial conditions. This psychological "attractor" represents the cumulative action of the cognitive operations used by the pigeon in solving and performing this search task. The results are discussed regarding their implications for visual cognition in pigeons and the usefulness of adaptive, subject-driven experimentation for investigating human and animal cognition more generally.

  17. Improved reactor regulating system logical architecture using genetic algorithm

    Directory of Open Access Journals (Sweden)

    Hyo-Sub Shim

    2017-12-01

    Full Text Available An improved Reactor Regulating System (RRS logic architecture, which is combined with genetic algorithm (GA, is implemented in this work. It is devised to provide an optimal solution to the current RRS. The current system works desirably and has contributed to safe and stable nuclear power plant operation. However, during the ascent and descent section of the reactor power, the RRS output reveals a relatively high steady-state error, and the output also carries a considerable level of overshoot. In an attempt to consolidate conservatism and minimize the error, this work proposes to apply GA to RRS and suggests reconfiguring the system. Prior to the use of GA, reverse engineering is implemented to build a Simulink-based RRS model. Reengineering is followed to produce a newly configured RRS to generate an output that has a reduced steady-state error and diminished overshoot level. A full-scope APR1400 simulator is used to examine the dynamic behaviors of RRS and to build the RRS Simulink model.

  18. Optimal Intermittent Dose Schedules for Chemotherapy Using Genetic Algorithm

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    Nadia ALAM

    2013-08-01

    Full Text Available In this paper, a design method for optimal cancer chemotherapy schedules via genetic algorithm (GA is presented. The design targets the key objective of chemotherapy to minimize the size of cancer tumor after a predefined time with keeping toxic side effects in limit. This is a difficult target to achieve using conventional clinical methods due to poor therapeutic indices of existing anti-cancer drugs. Moreover, there are clinical limitations in treatment administration to maintain continuous treatment. Besides, carefully decided rest periods are recommended to for patient’s comfort. Three intermittent drug scheduling schemes are presented in this paper where GA is used to optimize the dose quantities and timings by satisfying several treatment constraints. All three schemes are found to be effective in total elimination of cancer tumor after an agreed treatment length. The number of cancer cells is found zero at the end of the treatment for all three cases with tolerable toxicity. Finally, two of the schemes, “Fixed interval variable dose (FIVD and “Periodic dose” that are periodic in characteristic have been emphasized due to their additional simplicity in administration along with friendliness to patients. responses to the designed treatment schedules. Therefore the proposed design method is capable of planning effective, simple, patient friendly and acceptable chemotherapy schedules.

  19. Shape: automatic conformation prediction of carbohydrates using a genetic algorithm

    Directory of Open Access Journals (Sweden)

    Rosen Jimmy

    2009-09-01

    Full Text Available Abstract Background Detailed experimental three dimensional structures of carbohydrates are often difficult to acquire. Molecular modelling and computational conformation prediction are therefore commonly used tools for three dimensional structure studies. Modelling procedures generally require significant training and computing resources, which is often impractical for most experimental chemists and biologists. Shape has been developed to improve the availability of modelling in this field. Results The Shape software package has been developed for simplicity of use and conformation prediction performance. A trivial user interface coupled to an efficient genetic algorithm conformation search makes it a powerful tool for automated modelling. Carbohydrates up to a few hundred atoms in size can be investigated on common computer hardware. It has been shown to perform well for the prediction of over four hundred bioactive oligosaccharides, as well as compare favourably with previously published studies on carbohydrate conformation prediction. Conclusion The Shape fully automated conformation prediction can be used by scientists who lack significant modelling training, and performs well on computing hardware such as laptops and desktops. It can also be deployed on computer clusters for increased capacity. The prediction accuracy under the default settings is good, as it agrees well with experimental data and previously published conformation prediction studies. This software is available both as open source and under commercial licenses.

  20. Optimum detailed design of reinforced concrete frames using genetic algorithms

    Science.gov (United States)

    Govindaraj, V.; Ramasamy, J. V.

    2007-06-01

    This article presents the application of the genetic algorithm to the optimum detailed design of reinforced concrete frames based on Indian Standard specifications. The objective function is the total cost of the frame which includes the cost of concrete, formwork and reinforcing steel for individual members of the frame. In order for the optimum design to be directly constructible without any further modifications, aspects such as available standard reinforcement bar diameters, spacing requirements of reinforcing bars, modular sizes of members, architectural requirements on member sizes and other practical requirements in addition to relevant codal provisions are incorporated into the optimum design model. The produced optimum design satisfies the strength, serviceability, ductility, durability and other constraints related to good design and detailing practice. The detailing of reinforcements in the beam members is carried out as a sub-level optimization problem. This strategy helps to reduce the size of the optimization problem and saves computational time. The proposed method is demonstrated through several example problems and the optimum results obtained are compared with those in the available literature. It is concluded that the proposed optimum design model can be adopted in design offices as it yields rational, reliable, economical, time-saving and practical designs.

  1. Epidemic Modelling by Ripple-Spreading Network and Genetic Algorithm

    Directory of Open Access Journals (Sweden)

    Jian-Qin Liao

    2013-01-01

    Full Text Available Mathematical analysis and modelling is central to infectious disease epidemiology. This paper, inspired by the natural ripple-spreading phenomenon, proposes a novel ripple-spreading network model for the study of infectious disease transmission. The new epidemic model naturally has good potential for capturing many spatial and temporal features observed in the outbreak of plagues. In particular, using a stochastic ripple-spreading process simulates the effect of random contacts and movements of individuals on the probability of infection well, which is usually a challenging issue in epidemic modeling. Some ripple-spreading related parameters such as threshold and amplifying factor of nodes are ideal to describe the importance of individuals’ physical fitness and immunity. The new model is rich in parameters to incorporate many real factors such as public health service and policies, and it is highly flexible to modifications. A genetic algorithm is used to tune the parameters of the model by referring to historic data of an epidemic. The well-tuned model can then be used for analyzing and forecasting purposes. The effectiveness of the proposed method is illustrated by simulation results.

  2. Optimal vaccination schedule search using genetic algorithm over MPI technology

    Directory of Open Access Journals (Sweden)

    Calonaci Cristiano

    2012-11-01

    Full Text Available Abstract Background Immunological strategies that achieve the prevention of tumor growth are based on the presumption that the immune system, if triggered before tumor onset, could be able to defend from specific cancers. In supporting this assertion, in the last decade active immunization approaches prevented some virus-related cancers in humans. An immunopreventive cell vaccine for the non-virus-related human breast cancer has been recently developed. This vaccine, called Triplex, targets the HER-2-neu oncogene in HER-2/neu transgenic mice and has shown to almost completely prevent HER-2/neu-driven mammary carcinogenesis when administered with an intensive and life-long schedule. Methods To better understand the preventive efficacy of the Triplex vaccine in reduced schedules we employed a computational approach. The computer model developed allowed us to test in silico specific vaccination schedules in the quest for optimality. Specifically here we present a parallel genetic algorithm able to suggest optimal vaccination schedule. Results & Conclusions The enormous complexity of combinatorial space to be explored makes this approach the only possible one. The suggested schedule was then tested in vivo, giving good results. Finally, biologically relevant outcomes of optimization are presented.

  3. Offspring Generation Method for interactive Genetic Algorithm considering Multimodal Preference

    Science.gov (United States)

    Ito, Fuyuko; Hiroyasu, Tomoyuki; Miki, Mitsunori; Yokouchi, Hisatake

    In interactive genetic algorithms (iGAs), computer simulations prepare design candidates that are then evaluated by the user. Therefore, iGA can predict a user's preferences. Conventional iGA problems involve a search for a single optimum solution, and iGA were developed to find this single optimum. On the other hand, our target problems have several peaks in a function and there are small differences among these peaks. For such problems, it is better to show all the peaks to the user. Product recommendation in shopping sites on the web is one example of such problems. Several types of preference trend should be prepared for users in shopping sites. Exploitation and exploration are important mechanisms in GA search. To perform effective exploitation, the offspring generation method (crossover) is very important. Here, we introduced a new offspring generation method for iGA in multimodal problems. In the proposed method, individuals are clustered into subgroups and offspring are generated in each group. The proposed method was applied to an experimental iGA system to examine its effectiveness. In the experimental iGA system, users can decide on preferable t-shirts to buy. The results of the subjective experiment confirmed that the proposed method enables offspring generation with consideration of multimodal preferences, and the proposed mechanism was also shown not to adversely affect the performance of preference prediction.

  4. Shape: automatic conformation prediction of carbohydrates using a genetic algorithm

    Science.gov (United States)

    2009-01-01

    Background Detailed experimental three dimensional structures of carbohydrates are often difficult to acquire. Molecular modelling and computational conformation prediction are therefore commonly used tools for three dimensional structure studies. Modelling procedures generally require significant training and computing resources, which is often impractical for most experimental chemists and biologists. Shape has been developed to improve the availability of modelling in this field. Results The Shape software package has been developed for simplicity of use and conformation prediction performance. A trivial user interface coupled to an efficient genetic algorithm conformation search makes it a powerful tool for automated modelling. Carbohydrates up to a few hundred atoms in size can be investigated on common computer hardware. It has been shown to perform well for the prediction of over four hundred bioactive oligosaccharides, as well as compare favourably with previously published studies on carbohydrate conformation prediction. Conclusion The Shape fully automated conformation prediction can be used by scientists who lack significant modelling training, and performs well on computing hardware such as laptops and desktops. It can also be deployed on computer clusters for increased capacity. The prediction accuracy under the default settings is good, as it agrees well with experimental data and previously published conformation prediction studies. This software is available both as open source and under commercial licenses. PMID:20298520

  5. Genetic Algorithm-based Affine Parameter Estimation for Shape Recognition

    Directory of Open Access Journals (Sweden)

    Yuxing Mao

    2014-06-01

    Full Text Available Shape recognition is a classically difficult problem because of the affine transformation between two shapes. The current study proposes an affine parameter estimation method for shape recognition based on a genetic algorithm (GA. The contributions of this study are focused on the extraction of affine-invariant features, the individual encoding scheme, and the fitness function construction policy for a GA. First, the affine-invariant characteristics of the centroid distance ratios (CDRs of any two opposite contour points to the barycentre are analysed. Using different intervals along the azimuth angle, the different numbers of CDRs of two candidate shapes are computed as representations of the shapes, respectively. Then, the CDRs are selected based on predesigned affine parameters to construct the fitness function. After that, a GA is used to search for the affine parameters with optimal matching between candidate shapes, which serve as actual descriptions of the affine transformation between the shapes. Finally, the CDRs are resampled based on the estimated parameters to evaluate the similarity of the shapes for classification. The experimental results demonstrate the robust performance of the proposed method in shape recognition with translation, scaling, rotation and distortion.

  6. Optimization of the marinelli beaker dimensions using genetic algorithm.

    Science.gov (United States)

    Zamzamian, Seyed Mehrdad; Hosseini, Seyed Abolfazl; Samadfam, Mohammad

    2017-06-01

    A computational code, based on the genetic algorithm and MCNPX version 2.6 code was developed and used to investigate the effects of some important parameters of HPGe detector (such as Al cap thickness, dead-layer thickness and Ge hole size) on optimum dimensions of marinelli beaker. In addition, the effects of detector material on optimal beaker dimensions were also investigated. Finally, the optimized beaker dimensions at various beaker volumes (300, 500, 700, 1000 and 1500 cm(3)) were determined for some conventional Ge detectors with different crystal sizes (16 sizes). These sets of data then were used to drive mathematical formulas (obtained by best fitting to data sets). The results showed that, there is no meaningful correlation between the optimum dimensions of the beaker and each of the dead-layer thickness, Al cap thickness and the Ge-crystal hole size. On the other hand, the optimum beaker radius increases with decreasing the density of the detector material while the beaker height decreases. Copyright © 2017 Elsevier Ltd. All rights reserved.

  7. NMR assignments of sparsely labeled proteins using a genetic algorithm.

    Science.gov (United States)

    Gao, Qi; Chalmers, Gordon R; Moremen, Kelley W; Prestegard, James H

    2017-04-01

    Sparse isotopic labeling of proteins for NMR studies using single types of amino acid ((15)N or (13)C enriched) has several advantages. Resolution is enhanced by reducing numbers of resonances for large proteins, and isotopic labeling becomes economically feasible for glycoproteins that must be expressed in mammalian cells. However, without access to the traditional triple resonance strategies that require uniform isotopic labeling, NMR assignment of crosspeaks in heteronuclear single quantum coherence (HSQC) spectra is challenging. We present an alternative strategy which combines readily accessible NMR data with known protein domain structures. Based on the structures, chemical shifts are predicted, NOE cross-peak lists are generated, and residual dipolar couplings (RDCs) are calculated for each labeled site. Simulated data are then compared to measured values for a trial set of assignments and scored. A genetic algorithm uses the scores to search for an optimal pairing of HSQC crosspeaks with labeled sites. While none of the individual data types can give a definitive assignment for a particular site, their combination can in most cases. Four test proteins previously assigned using triple resonance methods and a sparsely labeled glycosylated protein, Robo1, previously assigned by manual analysis, are used to validate the method and develop a criterion for identifying sites assigned with high confidence.

  8. An efficient genetic algorithm for structure prediction at the nanoscale.

    Science.gov (United States)

    Lazauskas, Tomas; Sokol, Alexey A; Woodley, Scott M

    2017-03-17

    We have developed and implemented a new global optimization technique based on a Lamarckian genetic algorithm with the focus on structure diversity. The key process in the efficient search on a given complex energy landscape proves to be the removal of duplicates that is achieved using a topological analysis of candidate structures. The careful geometrical prescreening of newly formed structures and the introduction of new mutation move classes improve the rate of success further. The power of the developed technique, implemented in the Knowledge Led Master Code, or KLMC, is demonstrated by its ability to locate and explore a challenging double funnel landscape of a Lennard-Jones 38 atom system (LJ38). We apply the redeveloped KLMC to investigate three chemically different systems: ionic semiconductor (ZnO)1-32, metallic Ni13 and covalently bonded C60. All four systems have been systematically explored on the energy landscape defined using interatomic potentials. The new developments allowed us to successfully locate the double funnels of LJ38, find new local and global minima for ZnO clusters, extensively explore the Ni13 and C60 (the buckminsterfullerene, or buckyball) potential energy surfaces.

  9. Automated design of infrared digital metamaterials by genetic algorithm

    Science.gov (United States)

    Sugino, Yuya; Ishikawa, Atsushi; Hayashi, Yasuhiko; Tsuruta, Kenji

    2017-08-01

    We demonstrate automatic design of infrared (IR) metamaterials using a genetic algorithm (GA) and experimentally characterize their IR properties. To implement the automated design scheme of the metamaterial structures, we adopt a digital metamaterial consisting of 7 × 7 Au nano-pixels with an area of 200 nm × 200 nm, and their placements are coded as binary genes in the GA optimization process. The GA combined with three-dimensional (3D) finite element method (FEM) simulation is developed and applied to automatically construct a digital metamaterial to exhibit pronounced plasmonic resonances at the target IR frequencies. Based on the numerical results, the metamaterials are fabricated on a Si substrate over an area of 1 mm × 1 mm by using an EB lithography, Cr/Au (2/20 nm) depositions, and liftoff process. In the FT-IR measurement, pronounced plasmonic responses of each metamaterial are clearly observed near the targeted frequencies, although the synthesized pixel arrangements of the metamaterials are seemingly random. The corresponding numerical simulations reveal the important resonant behavior of each pixel and their hybridized systems. Our approach is fully computer-aided without artificial manipulation, thus paving the way toward the novel device design for next-generation plasmonic device applications.

  10. Experimental optimization of protein refolding with a genetic algorithm

    Science.gov (United States)

    Anselment, Bernd; Baerend, Danae; Mey, Elisabeth; Buchner, Johannes; Weuster-Botz, Dirk; Haslbeck, Martin

    2010-01-01

    Refolding of proteins from solubilized inclusion bodies still represents a major challenge for many recombinantly expressed proteins and often constitutes a major bottleneck. As in vitro refolding is a complex reaction with a variety of critical parameters, suitable refolding conditions are typically derived empirically in extensive screening experiments. Here, we introduce a new strategy that combines screening and optimization of refolding yields with a genetic algorithm (GA). The experimental setup was designed to achieve a robust and universal method that should allow optimizing the folding of a variety of proteins with the same routine procedure guided by the GA. In the screen, we incorporated a large number of common refolding additives and conditions. Using this design, the refolding of four structurally and functionally different model proteins was optimized experimentally, achieving 74–100% refolding yield for all of them. Interestingly, our results show that this new strategy provides optimum conditions not only for refolding but also for the activity of the native enzyme. It is designed to be generally applicable and seems to be eligible for all enzymes. PMID:20799347

  11. Optimizing SRF Gun Cavity Profiles in a Genetic Algorithm Framework

    Energy Technology Data Exchange (ETDEWEB)

    Alicia Hofler, Pavel Evtushenko, Frank Marhauser

    2009-09-01

    Automation of DC photoinjector designs using a genetic algorithm (GA) based optimization is an accepted practice in accelerator physics. Allowing the gun cavity field profile shape to be varied can extend the utility of this optimization methodology to superconducting and normal conducting radio frequency (SRF/RF) gun based injectors. Finding optimal field and cavity geometry configurations can provide guidance for cavity design choices and verify existing designs. We have considered two approaches for varying the electric field profile. The first is to determine the optimal field profile shape that should be used independent of the cavity geometry, and the other is to vary the geometry of the gun cavity structure to produce an optimal field profile. The first method can provide a theoretical optimal and can illuminate where possible gains can be made in field shaping. The second method can produce more realistically achievable designs that can be compared to existing designs. In this paper, we discuss the design and implementation for these two methods for generating field profiles for SRF/RF guns in a GA based injector optimization scheme and provide preliminary results.

  12. Application of genetic algorithm on optimization of laser beam shaping.

    Science.gov (United States)

    Tsai, Cheng-Mu; Fang, Yi-Chin; Lin, Chia-Te

    2015-06-15

    This study proposes a newly developed optimization method for an aspherical lens system employed in a refractive laser beam shaping system, which performs transformations on laser spots such that they are transformed into flat-tops of any size. In this paper, a genetic algorithm (GA) with multipoint search is proposed as the optimization method, together with macro language in optical simulation software, in order to search for ideal and optimized parameters. In comparison to a traditional two-dimensional (2D) computational method, using the one-dimensional (1D) computation for laser beam shaping can search for the optimal solution approximately twice as fast (after experiments). The optimal results show that when the laser spot shrinks from 3 mm to 1.07 mm, 88% uniformity is achieved, and when the laser spot increases from 3 mm to 5.273 mm, 90% uniformity is achieved. The distances between the lenses for both systems described above are even smaller than the thickness for the first lens, enabling us to conclude that our design objectives of extra light and slimness in the system are achieved.

  13. Optimization of a Genetic Algorithm for the Functionalization of Fullerenes.

    Science.gov (United States)

    Addicoat, Matthew A; Page, Alister J; Brain, Zoe E; Flack, Lloyd; Morokuma, Keiji; Irle, Stephan

    2012-05-08

    We present the optimization of a genetic algorithm (GA) that is designed to predict the most stable structural isomers of hydrogenated and hydroxylated fullerene cages. Density functional theory (DFT) and density functional tight binding (DFTB) methods are both employed to compute isomer energies. We show that DFTB and DFT levels of theory are in good agreement with each other and that therefore both sets of optimized GA parameters are very similar. As a prototypical fullerene cage, we consider the functionalization of the C20 species, since for this smallest possible fullerene cage it is possible to compute all possible isomer energies for evaluation of the GA performance. An energy decomposition analysis for both C20Hn and C20(OH)n systems reveals that, for only few functional groups, the relative stabilities of different structural isomers may be rationalized simply with recourse to π-Hückel theory. However, upon a greater degree of functionalization, π-electronic effects alone are incapable of describing the interaction between the functional groups and the distorted cage, and both σ- and π-electronic structure must be taken into account in order to understand the relative isomer stabilities.

  14. Toward Developing Genetic Algorithms to Aid in Critical Infrastructure Modeling

    Energy Technology Data Exchange (ETDEWEB)

    2007-05-01

    Today’s society relies upon an array of complex national and international infrastructure networks such as transportation, telecommunication, financial and energy. Understanding these interdependencies is necessary in order to protect our critical infrastructure. The Critical Infrastructure Modeling System, CIMS©, examines the interrelationships between infrastructure networks. CIMS© development is sponsored by the National Security Division at the Idaho National Laboratory (INL) in its ongoing mission for providing critical infrastructure protection and preparedness. A genetic algorithm (GA) is an optimization technique based on Darwin’s theory of evolution. A GA can be coupled with CIMS© to search for optimum ways to protect infrastructure assets. This includes identifying optimum assets to enforce or protect, testing the addition of or change to infrastructure before implementation, or finding the optimum response to an emergency for response planning. This paper describes the addition of a GA to infrastructure modeling for infrastructure planning. It first introduces the CIMS© infrastructure modeling software used as the modeling engine to support the GA. Next, the GA techniques and parameters are defined. Then a test scenario illustrates the integration with CIMS© and the preliminary results.

  15. Development of Double Layer Microwave Absorber Using Genetic Algorithm

    Science.gov (United States)

    Kumar, Abhishek; Singh, Samarjit; Singh, Dharmendra

    2017-09-01

    In this paper, an efficient two-layer microwave absorber at X-band is designed, optimized and implemented using the available materials with frequency dependent complex permittivity and complex permeability values as material database. The present work is focused on the design of a two-layer microwave absorber with good microwave absorption properties combined with broadband features at X-band. The optimization of various parameters such as materials, their sequence and thickness for obtaining better microwave absorption characteristics at X-band has been realized using Genetic Algorithm (GA). The optimized results were used to design a two-layer microwave absorber and experimentally tested using Attenuation Testing Device (ATD). Further verification of the experimentally obtained absorption results were simulated in High Frequency Structure Simulator (HFSS). The ATD result show that the maximum Reflection Loss (RL) for two-layer microwave absorber was -21.98 dB with 2.77 GHz bandwidth (corresponding to -10 dB) at 11.06 GHz for a total coating thickness of 1.5 mm.

  16. Study on the Method of Association Rules Mining Based on Genetic Algorithm and Application in Analysis of Seawater Samples

    Directory of Open Access Journals (Sweden)

    Qiuhong Sun

    2014-04-01

    Full Text Available Based on the data mining research, the data mining based on genetic algorithm method, the genetic algorithm is briefly introduced, while the genetic algorithm based on two important theories and theoretical templates principle implicit parallelism is also discussed. Focuses on the application of genetic algorithms for association rule mining method based on association rule mining, this paper proposes a genetic algorithm fitness function structure, data encoding, such as the title of the improvement program, in particular through the early issues study, proposed the improved adaptive Pc, Pm algorithm is applied to the genetic algorithm, thereby improving efficiency of the algorithm. Finally, a genetic algorithm based association rule mining algorithm, and be applied in sea water samples database in data mining and prove its effective.

  17. Binocular self-calibration performed via adaptive genetic algorithm based on laser line imaging

    Science.gov (United States)

    Apolinar Muñoz Rodríguez, J.; Mejía Alanís, Francisco Carlos

    2016-07-01

    An accurate technique to perform binocular self-calibration by means of an adaptive genetic algorithm based on a laser line is presented. In this calibration, the genetic algorithm computes the vision parameters through simulated binary crossover (SBX). To carry it out, the genetic algorithm constructs an objective function from the binocular geometry of the laser line projection. Then, the SBX minimizes the objective function via chromosomes recombination. In this algorithm, the adaptive procedure determines the search space via line position to obtain the minimum convergence. Thus, the chromosomes of vision parameters provide the minimization. The approach of the proposed adaptive genetic algorithm is to calibrate and recalibrate the binocular setup without references and physical measurements. This procedure leads to improve the traditional genetic algorithms, which calibrate the vision parameters by means of references and an unknown search space. It is because the proposed adaptive algorithm avoids errors produced by the missing of references. Additionally, the three-dimensional vision is carried out based on the laser line position and vision parameters. The contribution of the proposed algorithm is corroborated by an evaluation of accuracy of binocular calibration, which is performed via traditional genetic algorithms.

  18. Container traffic prediction in a seaport stockyard implementing artificial bee colony algorithm compared with the genetic algorithm

    OpenAIRE

    Gökkuş, Ümit; Akoğlu, Kıvanç; Yıldırım, Mehmet

    2017-01-01

    Container traffic prediction in a seaport stockyard implementing artificial bee colony algorithm compared with the genetic algorithmAfter finishing the construction stage of a container terminal, it is very difficult to modify the current port configuration and properties. For this instance, to avoid of the sunk costs, it is crucial for the seaport designers to predict the container traffic at any stage of the design phase. One of the important facilities of a seaport is the container stockya...

  19. Discharges Classification using Genetic Algorithms and Feature Selection Algorithms on Time and Frequency Domain Data Extracted from Leakage Current Measurements

    OpenAIRE

    D. Pylarinos; Theofilatos, K.; K. Siderakis; E. Thalassinakis

    2013-01-01

    A number of 387 discharge portraying waveforms recorded on 18 different 150 kV post insulators installed at two different Substations in Crete, Greece are considered in this paper. Twenty different features are extracted from each waveform and two feature selection algorithms (t-test and mRMR) are employed. Genetic algorithms are used to classify waveforms in two different classes related to the portrayed discharges. Five different data sets are employed (1. the original feature vector, 2. ti...

  20. Sequential and Mixed Genetic Algorithm and Learning Automata (SGALA, MGALA) for Feature Selection in QSAR.

    Science.gov (United States)

    MotieGhader, Habib; Gharaghani, Sajjad; Masoudi-Sobhanzadeh, Yosef; Masoudi-Nejad, Ali

    2017-01-01

    Feature selection is of great importance in Quantitative Structure-Activity Relationship (QSAR) analysis. This problem has been solved using some meta-heuristic algorithms such as GA, PSO, ACO and so on. In this work two novel hybrid meta-heuristic algorithms i.e. Sequential GA and LA (SGALA) and Mixed GA and LA (MGALA), which are based on Genetic algorithm and learning automata for QSAR feature selection are proposed. SGALA algorithm uses advantages of Genetic algorithm and Learning Automata sequentially and the MGALA algorithm uses advantages of Genetic Algorithm and Learning Automata simultaneously. We applied our proposed algorithms to select the minimum possible number of features from three different datasets and also we observed that the MGALA and SGALA algorithms had the best outcome independently and in average compared to other feature selection algorithms. Through comparison of our proposed algorithms, we deduced that the rate of convergence to optimal result in MGALA and SGALA algorithms were better than the rate of GA, ACO, PSO and LA algorithms. In the end, the results of GA, ACO, PSO, LA, SGALA, and MGALA algorithms were applied as the input of LS-SVR model and the results from LS-SVR models showed that the LS-SVR model had more predictive ability with the input from SGALA and MGALA algorithms than the input from all other mentioned algorithms. Therefore, the results have corroborated that not only is the predictive efficiency of proposed algorithms better, but their rate of convergence is also superior to the all other mentioned algorithms.

  1. In-Space Radiator Shape Optimization using Genetic Algorithms

    Science.gov (United States)

    Hull, Patrick V.; Kittredge, Ken; Tinker, Michael; SanSoucie, Michael

    2006-01-01

    Future space exploration missions will require the development of more advanced in-space radiators. These radiators should be highly efficient and lightweight, deployable heat rejection systems. Typical radiators for in-space heat mitigation commonly comprise a substantial portion of the total vehicle mass. A small mass savings of even 5-10% can greatly improve vehicle performance. The objective of this paper is to present the development of detailed tools for the analysis and design of in-space radiators using evolutionary computation techniques. The optimality criterion is defined as a two-dimensional radiator with a shape demonstrating the smallest mass for the greatest overall heat transfer, thus the end result is a set of highly functional radiator designs. This cross-disciplinary work combines topology optimization and thermal analysis design by means of a genetic algorithm The proposed design tool consists of the following steps; design parameterization based on the exterior boundary of the radiator, objective function definition (mass minimization and heat loss maximization), objective function evaluation via finite element analysis (thermal radiation analysis) and optimization based on evolutionary algorithms. The radiator design problem is defined as follows: the input force is a driving temperature and the output reaction is heat loss. Appropriate modeling of the space environment is added to capture its effect on the radiator. The design parameters chosen for this radiator shape optimization problem fall into two classes, variable height along the width of the radiator and a spline curve defining the -material boundary of the radiator. The implementation of multiple design parameter schemes allows the user to have more confidence in the radiator optimization tool upon demonstration of convergence between the two design parameter schemes. This tool easily allows the user to manipulate the driving temperature regions thus permitting detailed design of in

  2. Classification and learning using genetic algorithms applications in Bioinformatics and Web Intelligence

    CERN Document Server

    Bandyopadhyay, Sanghamitra

    2007-01-01

    This book provides a unified framework that describes how genetic learning can be used to design pattern recognition and learning systems. It examines how a search technique, the genetic algorithm, can be used for pattern classification mainly through approximating decision boundaries. Coverage also demonstrates the effectiveness of the genetic classifiers vis-à-vis several widely used classifiers, including neural networks.

  3. Genetic algorithms with memory- and elitism-based immigrants in dynamic environments.

    Science.gov (United States)

    Yang, Shengxiang

    2008-01-01

    In recent years the genetic algorithm community has shown a growing interest in studying dynamic optimization problems. Several approaches have been devised. The random immigrants and memory schemes are two major ones. The random immigrants scheme addresses dynamic environments by maintaining the population diversity while the memory scheme aims to adapt genetic algorithms quickly to new environments by reusing historical information. This paper investigates a hybrid memory and random immigrants scheme, called memory-based immigrants, and a hybrid elitism and random immigrants scheme, called elitism-based immigrants, for genetic algorithms in dynamic environments. In these schemes, the best individual from memory or the elite from the previous generation is retrieved as the base to create immigrants into the population by mutation. This way, not only can diversity be maintained but it is done more efficiently to adapt genetic algorithms to the current environment. Based on a series of systematically constructed dynamic problems, experiments are carried out to compare genetic algorithms with the memory-based and elitism-based immigrants schemes against genetic algorithms with traditional memory and random immigrants schemes and a hybrid memory and multi-population scheme. The sensitivity analysis regarding some key parameters is also carried out. Experimental results show that the memory-based and elitism-based immigrants schemes efficiently improve the performance of genetic algorithms in dynamic environments.

  4. MIP Models and Hybrid Algorithms for Simultaneous Job Splitting and Scheduling on Unrelated Parallel Machines

    Science.gov (United States)

    Ozmutlu, H. Cenk

    2014-01-01

    We developed mixed integer programming (MIP) models and hybrid genetic-local search algorithms for the scheduling problem of unrelated parallel machines with job sequence and machine-dependent setup times and with job splitting property. The first contribution of this paper is to introduce novel algorithms which make splitting and scheduling simultaneously with variable number of subjobs. We proposed simple chromosome structure which is constituted by random key numbers in hybrid genetic-local search algorithm (GAspLA). Random key numbers are used frequently in genetic algorithms, but it creates additional difficulty when hybrid factors in local search are implemented. We developed algorithms that satisfy the adaptation of results of local search into the genetic algorithms with minimum relocation operation of genes' random key numbers. This is the second contribution of the paper. The third contribution of this paper is three developed new MIP models which are making splitting and scheduling simultaneously. The fourth contribution of this paper is implementation of the GAspLAMIP. This implementation let us verify the optimality of GAspLA for the studied combinations. The proposed methods are tested on a set of problems taken from the literature and the results validate the effectiveness of the proposed algorithms. PMID:24977204

  5. A weight based genetic algorithm for selecting views

    Science.gov (United States)

    Talebian, Seyed H.; Kareem, Sameem A.

    2013-03-01

    Data warehouse is a technology designed for supporting decision making. Data warehouse is made by extracting large amount of data from different operational systems; transforming it to a consistent form and loading it to the central repository. The type of queries in data warehouse environment differs from those in operational systems. In contrast to operational systems, the analytical queries that are issued in data warehouses involve summarization of large volume of data and therefore in normal circumstance take a long time to be answered. On the other hand, the result of these queries must be answered in a short time to enable managers to make decisions as short time as possible. As a result, an essential need in this environment is in improving the performances of queries. One of the most popular methods to do this task is utilizing pre-computed result of queries. In this method, whenever a new query is submitted by the user instead of calculating the query on the fly through a large underlying database, the pre-computed result or views are used to answer the queries. Although, the ideal option would be pre-computing and saving all possible views, but, in practice due to disk space constraint and overhead due to view updates it is not considered as a feasible choice. Therefore, we need to select a subset of possible views to save on disk. The problem of selecting the right subset of views is considered as an important challenge in data warehousing. In this paper we suggest a Weighted Based Genetic Algorithm (WBGA) for solving the view selection problem with two objectives.

  6. Application of genetic algorithm for hemodialysis schedule optimization.

    Science.gov (United States)

    Choi, Jin Woo; Lee, Hajeong; Lee, Jung Chan; Lee, Saram; Kim, Yon Su; Yoon, Hyung-Jin; Kim, Hee Chan

    2017-07-01

    The conventional hemodialysis (HD) schedule has been used for decades, even though new modalities have been introduced. Many reasons limit practices of frequent dialysis, such as patients' environments and unknown optimal schedules for each patient. This research provides a theoretical recommendation of HD schedule through genetic algorithm (GA). An end-stage renal disease (ESRD) with various dialysis conditions was modeled through a classic variable-volume two-compartment kinetic model to simulate an anuric patient, and GA was implemented to search for an optimal HD schedule for each individual considering and ignoring burden consumption of each dialysis session. The adequacy of the optimized HD schedules through GA was assessed with time average concentration (TAC) and time average deviation (TAD). While ignoring the burden of dialysis sessions, GA returned schedules with slightly improved values of adequacy criteria (EKRc and std Kt/V), compared to the conventional regular uniform HD schedules. The optimized HD schedules also showed decreased TAC and TAD values compared to the conventional regular uniform HD schedules. It showed that frequent dialysis resulted in more effective treatment and higher fitness values. However, when burden was considered, less frequent dialysis schedules showed better fitness value. Through this research, GA confirmed that at least 12h of dialysis should be conducted for a week. The optimized schedules from GA indicated that evenly distributing the intervals amongst sessions is efficient, and that scheduling a session at the start and end of a week is optimal to overcome a long weekend interval. The theoretical optimal schedule of HD may help distribution of frequent dialysis and provide more schedule options to patients. Copyright © 2017 Elsevier B.V. All rights reserved.

  7. The Frontiers of Real-coded Genetic Algorithms

    Science.gov (United States)

    Kobayashi, Shigenobu

    Real-coded genetic algorithms (RCGA) are expected to solve efficiently real parameter optimization problems of multimodality, parameter dependency, and ill-scale. Multi-parental crossovers such as the simplex crossover (SPX) and the UNDX-m as extensions of the unimodal normal distribution crossove (UNDX) show relatively good performance for RCGA. The minimal generation gap (MGG) is used widely as a generation alternation model for RCGA. However, the MGG is not suited for multi-parental crossovers. Both the SPX and the UNDX-m have their own drawbacks respectively. Therefore, RCGA composed of them cannot be applied to highly dimensional problems, because their hidden faults appear. This paper presents a new and robust faramework for RCGA. First, we propose a generation alternation model called JGG (just generation gap) suited for multi-parental crossovers. The JGG replaces parents with children completely every generation. To solve the asymmetry and bias of children distribution generated by the SPX and the UNDX-m, an enhanced SPX (e-SPX) and an enhanced UNDX (e-UNDX) are proposed. Moreover, we propose a crossover called REX(φ,n+k) as a generlization of the e-UNDX, where φ and n+k denote some probability distribution and the number of parents respectively. A concept of the globally descent direction (GDD) is introduced to handle the situations where the population does not cover any optimum. The GDD can be used under the big valley structure. Then, we propose REXstar as an extention of the REX(φ,n+k) that can generate children to the GDD efficiently. Several experiments show excellent performance and robustness of the REXstar. Finally, the future work is discussed.

  8. Synergy of Genetic Algorithm with Extensive Neighborhood Search for the Permutation Flowshop Scheduling Problem

    Directory of Open Access Journals (Sweden)

    Rong-Chang Chen

    2017-01-01

    Full Text Available The permutation flowshop scheduling problem (PFSP is an important issue in the manufacturing industry. The objective of this study is to minimize the total completion time of scheduling for minimum makespan. Although the hybrid genetic algorithms are popular for resolving PFSP, their local search methods were compromised by the local optimum which has poorer solutions. This study proposed a new hybrid genetic algorithm for PFSP which makes use of the extensive neighborhood search method. For evaluating the performance, results of this study were compared against other state-of-the-art hybrid genetic algorithms. The comparisons showed that the proposed algorithm outperformed the other algorithms. A significant 50% test instances achieved the known optimal solutions. The proposed algorithm is simple and easy to implement. It can be extended easily to apply to similar combinatorial optimization problems.

  9. Hybridizing Differential Evolution with a Genetic Algorithm for Color Image Segmentation

    Directory of Open Access Journals (Sweden)

    R. V. V. Krishna

    2016-10-01

    Full Text Available This paper proposes a hybrid of differential evolution and genetic algorithms to solve the color image segmentation problem. Clustering based color image segmentation algorithms segment an image by clustering the features of color and texture, thereby obtaining accurate prototype cluster centers. In the proposed algorithm, the color features are obtained using the homogeneity model. A new texture feature named Power Law Descriptor (PLD which is a modification of Weber Local Descriptor (WLD is proposed and further used as a texture feature for clustering. Genetic algorithms are competent in handling binary variables, while differential evolution on the other hand is more efficient in handling real parameters. The obtained texture feature is binary in nature and the color feature is a real value, which suits very well the hybrid cluster center optimization problem in image segmentation. Thus in the proposed algorithm, the optimum texture feature centers are evolved using genetic algorithms, whereas the optimum color feature centers are evolved using differential evolution.

  10. A genetic algorithm for the optimization of fiber angles in composite laminates

    Energy Technology Data Exchange (ETDEWEB)

    Hwang, Shun Fa; Hsu, Ya Chu [National Yunlin University of Science and Technology, Douliu (China); Chen, Yuder [Chung-Shan Institute of Science and Technology, Taichung (China)

    2014-08-15

    A genetic algorithm for the optimization of composite laminates is proposed in this work. The well-known roulette selection criterion, one-point crossover operator, and uniform mutation operator are used in this genetic algorithm to create the next population. To improve the hill-climbing capability of the algorithm, adaptive mechanisms designed to adjust the probabilities of the crossover and mutation operators are included, and the elite strategy is enforced to ensure the quality of the optimum solution. The proposed algorithm includes a new operator called the elite comparison, which compares and uses the differences in the design variables of the two best solutions to find possible combinations. This genetic algorithm is tested in four optimization problems of composite laminates. Specifically, the effect of the elite comparison operator is evaluated. Results indicate that the elite comparison operator significantly accelerates the convergence of the algorithm, which thus becomes a good candidate for the optimization of composite laminates.

  11. Simulating Evolution of Drosophila melanogaster Ebony Mutants Using a Genetic Algorithm

    DEFF Research Database (Denmark)

    Helles, Glennie

    2009-01-01

    Genetic algorithms are generally quite easy to understand and work with, and they are a popular choice in many cases. One area in which genetic algorithms are widely and successfully used is artificial life where they are used to simulate evolution of artificial creatures. However, despite...... their suggestive name, simplicity and popularity in artificial life, they do not seem to have gained a footing within the field of population genetics to simulate evolution of real organisms --- possibly because genetic algorithms are based on a rather crude simplification of the evolutionary mechanisms known...... today. However, in this paper we report how a standard genetic algorithm is used to successfully simulate evolution of ebony mutants in a population of Drosophila melanogaster (D.melanogaster). The results show a remarkable resemblance to the evolution observed in real biological experiments with ebony...

  12. Multiple depots vehicle routing based on the ant colony with the genetic algorithm

    Directory of Open Access Journals (Sweden)

    ChunYing Liu

    2013-09-01

    Full Text Available Purpose: the distribution routing plans of multi-depots vehicle scheduling problem will increase exponentially along with the adding of customers. So, it becomes an important studying trend to solve the vehicle scheduling problem with heuristic algorithm. On the basis of building the model of multi-depots vehicle scheduling problem, in order to improve the efficiency of the multiple depots vehicle routing, the paper puts forward a fusion algorithm on multiple depots vehicle routing based on the ant colony algorithm with genetic algorithm. Design/methodology/approach: to achieve this objective, the genetic algorithm optimizes the parameters of the ant colony algorithm. The fusion algorithm on multiple depots vehicle based on the ant colony algorithm with genetic algorithm is proposed. Findings: simulation experiment indicates that the result of the fusion algorithm is more excellent than the other algorithm, and the improved algorithm has better convergence effective and global ability. Research limitations/implications: in this research, there are some assumption that might affect the accuracy of the model such as the pheromone volatile factor, heuristic factor in each period, and the selected multiple depots. These assumptions can be relaxed in future work. Originality/value: In this research, a new method for the multiple depots vehicle routing is proposed. The fusion algorithm eliminate the influence of the selected parameter by optimizing the heuristic factor, evaporation factor, initial pheromone distribute, and have the strong global searching ability. The Ant Colony algorithm imports cross operator and mutation operator for operating the first best solution and the second best solution in every iteration, and reserves the best solution. The cross and mutation operator extend the solution space and improve the convergence effective and the global ability. This research shows that considering both the ant colony and genetic algorithm

  13. Optimal design and robustification of fuzzy-logic controllers for robotic manipulators using genetic algorithms

    CERN Document Server

    Moini, A

    2002-01-01

    In this paper, genetic algorithms are used in the design and robustification various mo el-ba ed/non-model-based fuzzy-logic controllers for robotic manipulators. It is demonstrated that genetic algorithms provide effective means of designing the optimal set of fuzzy rules as well as the optimal domains of associated fuzzy sets in a new class of model-based-fuzzy-logic controllers. Furthermore, it is shown that genetic algorithms are very effective in the optimal design and robustification of non-model-based multivariable fuzzy-logic controllers for robotic manipulators.

  14. Hitting times of local and global optima in genetic algorithms with very high selection pressure

    Directory of Open Access Journals (Sweden)

    Eremeev Anton V.

    2017-01-01

    Full Text Available The paper is devoted to upper bounds on the expected first hitting times of the sets of local or global optima for non-elitist genetic algorithms with very high selection pressure. The results of this paper extend the range of situations where the upper bounds on the expected runtime are known for genetic algorithms and apply, in particular, to the Canonical Genetic Algorithm. The obtained bounds do not require the probability of fitness-decreasing mutation to be bounded by a constant which is less than one.

  15. Using genetic algorithm feature selection in neural classification systems for image pattern recognition

    Directory of Open Access Journals (Sweden)

    Margarita R. Gamarra A.

    2012-09-01

    Full Text Available Pattern recognition performance depends on variations during extraction, selection and classification stages. This paper presents an approach to feature selection by using genetic algorithms with regard to digital image recognition and quality control. Error rate and kappa coefficient were used for evaluating the genetic algorithm approach Neural networks were used for classification, involving the features selected by the genetic algorithms. The neural network approach was compared to a K-nearest neighbor classifier. The proposed approach performed better than the other methods.

  16. The potential of genetic algorithms for conceptual design of rotor systems

    Science.gov (United States)

    Crossley, William A.; Wells, Valana L.; Laananen, David H.

    1993-01-01

    The capabilities of genetic algorithms as a non-calculus based, global search method make them potentially useful in the conceptual design of rotor systems. Coupling reasonably simple analysis tools to the genetic algorithm was accomplished, and the resulting program was used to generate designs for rotor systems to match requirements similar to those of both an existing helicopter and a proposed helicopter design. This provides a comparison with the existing design and also provides insight into the potential of genetic algorithms in design of new rotors.

  17. Design and optimization of pulsed Chemical Exchange Saturation Transfer MRI using a multiobjective genetic algorithm.

    Science.gov (United States)

    Yoshimaru, Eriko S; Randtke, Edward A; Pagel, Mark D; Cárdenas-Rodríguez, Julio

    2016-02-01

    Pulsed Chemical Exchange Saturation Transfer (CEST) MRI experimental parameters and RF saturation pulse shapes were optimized using a multiobjective genetic algorithm. The optimization was carried out for RF saturation duty cycles of 50% and 90%, and results were compared to continuous wave saturation and Gaussian waveform. In both simulation and phantom experiments, continuous wave saturation performed the best, followed by parameters and shapes optimized by the genetic algorithm and then followed by Gaussian waveform. We have successfully demonstrated that the genetic algorithm is able to optimize pulse CEST parameters and that the results are translatable to clinical scanners. Copyright © 2015 Elsevier Inc. All rights reserved.

  18. Double Motor Coordinated Control Based on Hybrid Genetic Algorithm and CMAC

    Science.gov (United States)

    Cao, Shaozhong; Tu, Ji

    A novel hybrid cerebellar model articulation controller (CMAC) and online adaptive genetic algorithm (GA) controller is introduced to control two Brushless DC motor (BLDCM) which applied in a biped robot. Genetic Algorithm simulates the random learning among the individuals of a group, and CMAC simulates the self-learning of an individual. To validate the ability and superiority of the novel algorithm, experiments have been done in MATLAB/SIMULINK. Analysis among GA, hybrid GA-CMAC and CMAC feed-forward control is also given. The results prove that the torque ripple of the coordinated control system is eliminated by using the hybrid GA-CMAC algorithm.

  19. Maximizing influence in a social network: Improved results using a genetic algorithm

    Science.gov (United States)

    Zhang, Kaiqi; Du, Haifeng; Feldman, Marcus W.

    2017-07-01

    The influence maximization problem focuses on finding a small subset of nodes in a social network that maximizes the spread of influence. While the greedy algorithm and some improvements to it have been applied to solve this problem, the long solution time remains a problem. Stochastic optimization algorithms, such as simulated annealing, are other choices for solving this problem, but they often become trapped in local optima. We propose a genetic algorithm to solve the influence maximization problem. Through multi-population competition, using this algorithm we achieve an optimal result while maintaining diversity of the solution. We tested our method with actual networks, and our genetic algorithm performed slightly worse than the greedy algorithm but better than other algorithms.

  20. A Hybrid Tabu Search and Algorithm Genetic for Solving the Economic Dispatch Problem

    Directory of Open Access Journals (Sweden)

    Bakhta NAAMA

    2013-06-01

    Full Text Available The application of optimization techniques to power system planning and operation problems has been an area of active research in the recent past. Genetic Algorithm (GA, Tabu Search (TS are widely used to combinatorial optimization in recent years. Combining the advantages of individual algorithms, a hybrid TS/GA algorithm to solve the economic dispatch problem is proposed in this paper, using the method of penalty to transform the problem ED with constraints in a simple problem without constraints. An IEEE 57-bus power system has been used to test the proposed algorithm. Comparing the results of the proposed algorithm with GA, TS and proposed TS/GA hybrid method has the strongest capability of finding global optimal solution within reasonable computing time. We then give a comparison between two algorithms hybrids (Tabu Search / Genetic Algorithm TS/GA and (Tabu Search/ quasi-Newton method TS/QN.

  1. Selection of training populations (and other subset selection problems) with an accelerated genetic algorithm (STPGA: An R-package for selection of training populations with a genetic algorithm)

    OpenAIRE

    Akdemir, Deniz

    2017-01-01

    Optimal subset selection is an important task that has numerous algorithms designed for it and has many application areas. STPGA contains a special genetic algorithm supplemented with a tabu memory property (that keeps track of previously tried solutions and their fitness for a number of iterations), and with a regression of the fitness of the solutions on their coding that is used to form the ideal estimated solution (look ahead property) to search for solutions of generic optimal subset sel...

  2. Genetic Algorithms for a Parameter Estimation of a Fermentation Process Model: A Comparison

    Directory of Open Access Journals (Sweden)

    Olympia Roeva

    2005-12-01

    Full Text Available In this paper the problem of a parameter estimation using genetic algorithms is examined. A case study considering the estimation of 6 parameters of a nonlinear dynamic model of E. coli fermentation is presented as a test problem. The parameter estimation problem is stated as a nonlinear programming problem subject to nonlinear differential-algebraic constraints. This problem is known to be frequently ill-conditioned and multimodal. Thus, traditional (gradient-based local optimization methods fail to arrive satisfied solutions. To overcome their limitations, the use of different genetic algorithms as stochastic global optimization methods is explored. These algorithms are proved to be very suitable for the optimization of highly non-linear problems with many variables. Genetic algorithms can guarantee global optimality and robustness. These facts make them advantageous in use for parameter identification of fermentation models. A comparison between simple, modified and multi-population genetic algorithms is presented. The best result is obtained using the modified genetic algorithm. The considered algorithms converged very closely to the cost value but the modified algorithm is in times faster than other two.

  3. Evolving a Nelder-Mead Algorithm for Optimization with Genetic Programming.

    Science.gov (United States)

    Fajfar, Iztok; Puhan, Janez; Bűrmen, Árpád

    2017-01-01

    We used genetic programming to evolve a direct search optimization algorithm, similar to that of the standard downhill simplex optimization method proposed by Nelder and Mead ( 1965 ). In the training process, we used several ten-dimensional quadratic functions with randomly displaced parameters and different randomly generated starting simplices. The genetically obtained optimization algorithm showed overall better performance than the original Nelder-Mead method on a standard set of test functions. We observed that many parts of the genetically produced algorithm were seldom or never executed, which allowed us to greatly simplify the algorithm by removing the redundant parts. The resulting algorithm turns out to be considerably simpler than the original Nelder-Mead method while still performing better than the original method.

  4. The application of new simulated annealing genetic algorithm in film characters measurement

    Science.gov (United States)

    Chu, Dong; Gong, Xing-zhi; Cheng, Liang; Yu, Fei-hong

    2009-11-01

    A new global optimization method for thin film thickness calculation is presented in this paper. A new adaptive simulated annealing (ASA) combining with genetic algorithm is brought out. Adding traditional genetic algorithm to simulated annealing process avoids the premature convergence problem and improves the ability of global searching effectively. Firstly it generates a new group of local optimums through genetic and mutation operation; then the solutions are accepted at a certain probability, and will be calculated respectively in ASA process as the initial solutions. At last conjugate gradient algorithm is utilized to search accurately and quickly. This new ASGA can effectively enhance the robustness of the algorithm and reduce the limitation of the searching area. The experimental results show that the proposed algorithm can calculate the thickness of single layer and 2-layer thin film within the range of 10nm to 1μ m and less than 4% calculation error.

  5. Mean-Adaptive Real-Coding Genetic Algorithm and its Applications to Electromagnetic Optimization (Part One

    Directory of Open Access Journals (Sweden)

    Z. Raida

    2007-09-01

    Full Text Available In the paper, a novel instance of the real-coding steady-state genetic algorithm, called the Mean-adaptive real-coding genetic algorithm, is put forward. In this instance, three novel implementations of evolution operators are incorporated. Those are a recombination and two mutation operators. All of the evolution operators are designed with the aim of possessing a big explorative power. Moreover, one of the mutation operators exhibits self-adaptive behavior and the other exhibits adaptive behavior, thereby allowing the algorithm to self-control its own mutability as the search advances. This algorithm also takes advantage of population-elitist selection, acting as a replacement policy, being adopted from evolution strategies. The purpose of this paper (i.e., the first part is to provide theoretical foundations of a robust and advanced instance of the real-coding genetic algorithm having the big potential of being successfully applied to electromagnetic optimization.

  6. A Genetic-Algorithms-Based Approach for Programming Linear and Quadratic Optimization Problems with Uncertainty

    Directory of Open Access Journals (Sweden)

    Weihua Jin

    2013-01-01

    Full Text Available This paper proposes a genetic-algorithms-based approach as an all-purpose problem-solving method for operation programming problems under uncertainty. The proposed method was applied for management of a municipal solid waste treatment system. Compared to the traditional interactive binary analysis, this approach has fewer limitations and is able to reduce the complexity in solving the inexact linear programming problems and inexact quadratic programming problems. The implementation of this approach was performed using the Genetic Algorithm Solver of MATLAB (trademark of MathWorks. The paper explains the genetic-algorithms-based method and presents details on the computation procedures for each type of inexact operation programming problems. A comparison of the results generated by the proposed method based on genetic algorithms with those produced by the traditional interactive binary analysis method is also presented.

  7. Hybrid genetic algorithm in the Hopfield network for maximum 2-satisfiability problem

    Science.gov (United States)

    Kasihmuddin, Mohd Shareduwan Mohd; Sathasivam, Saratha; Mansor, Mohd. Asyraf

    2017-08-01

    Heuristic method was designed for finding optimal solution more quickly compared to classical methods which are too complex to comprehend. In this study, a hybrid approach that utilizes Hopfield network and genetic algorithm in doing maximum 2-Satisfiability problem (MAX-2SAT) was proposed. Hopfield neural network was used to minimize logical inconsistency in interpretations of logic clauses or program. Genetic algorithm (GA) has pioneered the implementation of methods that exploit the idea of combination and reproduce a better solution. The simulation incorporated with and without genetic algorithm will be examined by using Microsoft Visual 2013 C++ Express software. The performance of both searching techniques in doing MAX-2SAT was evaluate based on global minima ratio, ratio of satisfied clause and computation time. The result obtained form the computer simulation demonstrates the effectiveness and acceleration features of genetic algorithm in doing MAX-2SAT in Hopfield network.

  8. Computer aided selection in breeding programs using genetic algorithm in MATLAB program

    National Research Council Canada - National Science Library

    Davoodi Bojd, E; Soltanian Zadeh, H; Amiri, R; Hoori, M; Azimzadeh, M; Vahedi, S

    2010-01-01

    .... This can cause imperfections in the breeding procedures. In this paper an automatic method for simulating a population under natural selection is proposed based on the selection operator of the genetic algorithms...

  9. Genetic algorithm approach for adaptive power and subcarrier allocation in multi-user OFDM systems

    Science.gov (United States)

    Reddy, Y. B.; Naraghi-Pour, Mort

    2007-04-01

    In this paper, a novel genetic algorithm application is proposed for adaptive power and subcarrier allocation in multi-user Orthogonal Frequency Division Multiplexing (OFDM) systems. To test the application, a simple genetic algorithm was implemented in MATLAB language. With the goal of minimizing the overall transmit power while ensuring the fulfillment of each user's rate and bit error rate (BER) requirements, the proposed algorithm acquires the needed allocation through genetic search. The simulations were tested for BER 0.1 to 0.00001, data rate of 256 bit per OFDM block and chromosome length of 128. The results show that genetic algorithm outperforms the results in [3] in subcarrier allocation. The convergence of GA model with 8 users and 128 subcarriers performs better in power requirement compared to that in [4] but converges more slowly.

  10. Genetic Algorithm for Traveling Salesman Problem with Modified Cycle Crossover Operator

    Directory of Open Access Journals (Sweden)

    Abid Hussain

    2017-01-01

    Full Text Available Genetic algorithms are evolutionary techniques used for optimization purposes according to survival of the fittest idea. These methods do not ensure optimal solutions; however, they give good approximation usually in time. The genetic algorithms are useful for NP-hard problems, especially the traveling salesman problem. The genetic algorithm depends on selection criteria, crossover, and mutation operators. To tackle the traveling salesman problem using genetic algorithms, there are various representations such as binary, path, adjacency, ordinal, and matrix representations. In this article, we propose a new crossover operator for traveling salesman problem to minimize the total distance. This approach has been linked with path representation, which is the most natural way to represent a legal tour. Computational results are also reported with some traditional path representation methods like partially mapped and order crossovers along with new cycle crossover operator for some benchmark TSPLIB instances and found improvements.

  11. Analyzing the determinants of the voting behavior using a genetic algorithm

    National Research Council Canada - National Science Library

    Marcos Vizcaíno-González; Juan Pineiro-Chousa; M Ángeles López-Cabarcos

    2016-01-01

      Using data about votes emitted by funds in meetings held by United States banks from 2003 to 2013, we apply a genetic algorithm to a set of financial variables in order to detect the determinants of the vote direction...

  12. Genetic algorithm based PID controller design for a multi-area AGC ...

    African Journals Online (AJOL)

    user

    SMES) units to inject or absorb the active power of an interconnected power system. The functioning of Genetic Algorithm based PID controller has been tested on a 39-bus New England system and 75-bus Indian power system network.

  13. Genetic Algorithm for Initial Orbit Determination with Too Short Arc (Continued)

    Science.gov (United States)

    Li, Xin-ran; Wang, Xin

    2017-04-01

    When the genetic algorithm is used to solve the problem of too short-arc (TSA) orbit determination, due to the difference of computing process between the genetic algorithm and the classical method, the original method for outlier deletion is no longer applicable. In the genetic algorithm, the robust estimation is realized by introducing different loss functions for the fitness function, then the outlier problem of the TSA orbit determination is solved. Compared with the classical method, the genetic algorithm is greatly simplified by introducing in different loss functions. Through the comparison on the calculations of multiple loss functions, it is found that the least median square (LMS) estimation and least trimmed square (LTS) estimation can greatly improve the robustness of the TSA orbit determination, and have a high breakdown point.

  14. Calibration of parameters of water supply network model using genetic algorithm

    National Research Council Canada - National Science Library

    Tomasz Boczar; Norbert Adamikiewicz; Włodzimierz Stanisławski

    2017-01-01

    ...: the pressure on the node and volume flow in the network section. The first calibration method regards to application of the genetic algorithm, which is a build in plugin - “Epanet Calibrator...

  15. Design optimization of brushed permanent magnet D C motor by genetic algorithm

    CERN Document Server

    Amini, S

    2002-01-01

    Because of field winding replacement with permanent magnet in brushed permanent magnet D C (PMDC) motors, field losses are eliminated and the structure of the motor is more simple. Efficiency of these motors is therefore increased and the manufacturing process is simplified. Hence, these motors are commonly used in low power applications and their design and optimization is an important consideration. Genetic algorithms are proposed for design optimization of PMD motors because of their independence to objective function structure and its derivative. In this paper genetic algorithms are evaluated for PMDC motor design optimization. an introduction is first presented about PMDC motors, general design procedure and elements of their optimization. Genetic algorithms are then briefly described. Finally results of optimization by genetic algorithms are compared with the one obtained using a conventional method.

  16. Multi-heuristic dynamic task allocation using genetic algorithms in a heterogeneous distributed system

    Science.gov (United States)

    Page, Andrew J.; Keane, Thomas M.; Naughton, Thomas J.

    2010-01-01

    We present a multi-heuristic evolutionary task allocation algorithm to dynamically map tasks to processors in a heterogeneous distributed system. It utilizes a genetic algorithm, combined with eight common heuristics, in an effort to minimize the total execution time. It operates on batches of unmapped tasks and can preemptively remap tasks to processors. The algorithm has been implemented on a Java distributed system and evaluated with a set of six problems from the areas of bioinformatics, biomedical engineering, computer science and cryptography. Experiments using up to 150 heterogeneous processors show that the algorithm achieves better efficiency than other state-of-the-art heuristic algorithms. PMID:20862190

  17. Hybrid Model Based on Genetic Algorithms and SVM Applied to Variable Selection within Fruit Juice Classification

    Directory of Open Access Journals (Sweden)

    C. Fernandez-Lozano

    2013-01-01

    Full Text Available Given the background of the use of Neural Networks in problems of apple juice classification, this paper aim at implementing a newly developed method in the field of machine learning: the Support Vector Machines (SVM. Therefore, a hybrid model that combines genetic algorithms and support vector machines is suggested in such a way that, when using SVM as a fitness function of the Genetic Algorithm (GA, the most representative variables for a specific classification problem can be selected.

  18. Hybrid Model Based on Genetic Algorithms and SVM Applied to Variable Selection within Fruit Juice Classification

    Science.gov (United States)

    Fernandez-Lozano, C.; Canto, C.; Gestal, M.; Andrade-Garda, J. M.; Rabuñal, J. R.; Dorado, J.; Pazos, A.

    2013-01-01

    Given the background of the use of Neural Networks in problems of apple juice classification, this paper aim at implementing a newly developed method in the field of machine learning: the Support Vector Machines (SVM). Therefore, a hybrid model that combines genetic algorithms and support vector machines is suggested in such a way that, when using SVM as a fitness function of the Genetic Algorithm (GA), the most representative variables for a specific classification problem can be selected. PMID:24453933

  19. Truss Optimization for a Manned Nuclear Electric Space Vehicle using Genetic Algorithms

    Science.gov (United States)

    Benford, Andrew; Tinker, Michael L.

    2004-01-01

    The purpose of this paper is to utilize the genetic algorithm (GA) optimization method for structural design of a nuclear propulsion vehicle. Genetic algorithms provide a guided, random search technique that mirrors biological adaptation. To verify the GA capabilities, other traditional optimization methods were used to generate results for comparison to the GA results, first for simple two-dimensional structures, and then for full-scale three-dimensional truss designs.

  20. Aerodynamic Optimization of a Supersonic Bending Body Projectile by a Vector-Evaluated Genetic Algorithm

    Science.gov (United States)

    2016-12-01

    Physics and Industrial Engineering ; 2012; Beijing, China . ICAPIE 1875-3892. 26. Lipowski A, Lipowska D. Roulette-wheel selection via stochastic...Evaluated Genetic Algorithm prepared by Justin L Paul Academy of Applied Science 24 Warren Street Concord, NH 03301 under contract W911SR...Supersonic Bending Body Projectile by a Vector-Evaluated Genetic Algorithm prepared by Justin L Paul Academy of Applied Science 24 Warren Street

  1. Exploiting problem structure in a genetic algorithm approach to a nurse rostering problem

    OpenAIRE

    Aickelin, Uwe; Dowsland, Kathryn

    2008-01-01

    There is considerable interest in the use of genetic algorithms to solve problems arising in the areas of scheduling and timetabling. However, the classical genetic algorithm paradigm is not well equipped to handle the conflict between objectives and constraints that typically occurs in such problems. In order to overcome this, successful implementations frequently make use of problem specific knowledge. This paper is concerned with the development of a GA for a nurse rostering problem at a m...

  2. Framework for the Shape Optimization of Aerodynamic Profiles Using Genetic Algorithms

    Directory of Open Access Journals (Sweden)

    D. López

    2013-01-01

    Full Text Available This study developed a framework for the shape optimization of aerodynamics profiles using computational fluid dynamics (CFD and genetic algorithms. A genetic algorithm code and a commercial CFD code were integrated to develop a CFD shape optimization tool. The results obtained demonstrated the effectiveness of the developed tool. The shape optimization of airfoils was studied using different strategies to demonstrate the capacity of this tool with different GA parameter combinations.

  3. Multiobjective Economic Load Dispatch in 3-D Space by Genetic Algorithm

    Science.gov (United States)

    Jain, N. K.; Nangia, Uma; Singh, Iqbal

    2017-08-01

    This paper presents the application of genetic algorithm to Multiobjective Economic Load Dispatch (MELD) problem considering fuel cost, transmission losses and environmental pollution as objective functions. The MELD problem has been formulated using constraint method. The non-inferior set for IEEE 5, 14 and 30-bus system has been generated by using genetic algorithm and the target point has been obtained by using maximization of minimum relative attainments.

  4. Multiobjective Economic Load Dispatch in 3-D Space by Genetic Algorithm

    Science.gov (United States)

    Jain, N. K.; Nangia, Uma; Singh, Iqbal

    2017-10-01

    This paper presents the application of genetic algorithm to Multiobjective Economic Load Dispatch (MELD) problem considering fuel cost, transmission losses and environmental pollution as objective functions. The MELD problem has been formulated using constraint method. The non-inferior set for IEEE 5, 14 and 30-bus system has been generated by using genetic algorithm and the target point has been obtained by using maximization of minimum relative attainments.

  5. Genetic algorithm identification of a H-moving vector hysteresis model

    Energy Technology Data Exchange (ETDEWEB)

    Cardelli, E. [Department of Industrial Engineering, University of Perugia, I-06125 Perugia (Italy); Center for Electric and Magnetic Applied Research (Italy); Faba, A., E-mail: faba@unipg.it [Department of Industrial Engineering, University of Perugia, I-06125 Perugia (Italy); Center for Electric and Magnetic Applied Research (Italy)

    2014-02-15

    In this work we present an identification procedure for a vector hysteresis model defined by a H-moving approach. The model parameters are identified by means of a suitable implementation of a genetic algorithm with a set of experimental data. The analytical formulation of the model and the characteristics of the genetic algorithm used are described. A comparison between computed data and experimental measurements for a not oriented grain Si–Fe magnetic steel with a weak lamination anisotropy are reported.

  6. Genetic Local Search Algorithm for Optimization Design of Diffractive Optical Elements

    Science.gov (United States)

    Zhou, Guangya; Chen, Yixin; Wang, Zongguang; Song, Hongwei

    1999-07-01

    We propose a genetic local search algorithm (GLSA) for the optimization design of diffractive optical elements (DOE s). This hybrid algorithm incorporates advantages of both genetic algorithm (GA) and local search techniques. It appears better able to locate the global minimum compared with a canonical GA. Sample cases investigated here include the optimization design of binary-phase Dammann gratings, continuous surface-relief grating array generators, and a uniform top-hat focal plane intensity profile generator. Two GLSA s whose incorporated local search techniques are the hill-climbing method and the simulated annealing algorithm are investigated. Numerical experimental results demonstrate that the proposed algorithm is highly efficient and robust. DOE s that have high diffraction efficiency and excellent uniformity can be achieved by use of the algorithm we propose.

  7. A Swarm Optimization Genetic Algorithm Based on Quantum-Behaved Particle Swarm Optimization.

    Science.gov (United States)

    Sun, Tao; Xu, Ming-Hai

    2017-01-01

    Quantum-behaved particle swarm optimization (QPSO) algorithm is a variant of the traditional particle swarm optimization (PSO). The QPSO that was originally developed for continuous search spaces outperforms the traditional PSO in search ability. This paper analyzes the main factors that impact the search ability of QPSO and converts the particle movement formula to the mutation condition by introducing the rejection region, thus proposing a new binary algorithm, named swarm optimization genetic algorithm (SOGA), because it is more like genetic algorithm (GA) than PSO in form. SOGA has crossover and mutation operator as GA but does not need to set the crossover and mutation probability, so it has fewer parameters to control. The proposed algorithm was tested with several nonlinear high-dimension functions in the binary search space, and the results were compared with those from BPSO, BQPSO, and GA. The experimental results show that SOGA is distinctly superior to the other three algorithms in terms of solution accuracy and convergence.

  8. Application of Hybrid Genetic Algorithm Routine in Optimizing Food and Bioengineering Processes

    Directory of Open Access Journals (Sweden)

    Jaya Shankar Tumuluru

    2016-11-01

    Full Text Available Optimization is a crucial step in the analysis of experimental results. Deterministic methods only converge on local optimums and require exponentially more time as dimensionality increases. Stochastic algorithms are capable of efficiently searching the domain space; however convergence is not guaranteed. This article demonstrates the novelty of the hybrid genetic algorithm (HGA, which combines both stochastic and deterministic routines for improved optimization results. The new hybrid genetic algorithm developed is applied to the Ackley benchmark function as well as case studies in food, biofuel, and biotechnology processes. For each case study, the hybrid genetic algorithm found a better optimum candidate than reported by the sources. In the case of food processing, the hybrid genetic algorithm improved the anthocyanin yield by 6.44%. Optimization of bio-oil production using HGA resulted in a 5.06% higher yield. In the enzyme production process, HGA predicted a 0.39% higher xylanase yield. Hybridization of the genetic algorithm with a deterministic algorithm resulted in an improved optimum compared to statistical methods.

  9. Ensemble of hybrid genetic algorithm for two-dimensional phase unwrapping

    Science.gov (United States)

    Balakrishnan, D.; Quan, C.; Tay, C. J.

    2013-06-01

    The phase unwrapping is the final and trickiest step in any phase retrieval technique. Phase unwrapping by artificial intelligence methods (optimization algorithms) such as hybrid genetic algorithm, reverse simulated annealing, particle swarm optimization, minimum cost matching showed better results than conventional phase unwrapping methods. In this paper, Ensemble of hybrid genetic algorithm with parallel populations is proposed to solve the branch-cut phase unwrapping problem. In a single populated hybrid genetic algorithm, the selection, cross-over and mutation operators are applied to obtain new population in every generation. The parameters and choice of operators will affect the performance of the hybrid genetic algorithm. The ensemble of hybrid genetic algorithm will facilitate to have different parameters set and different choice of operators simultaneously. Each population will use different set of parameters and the offspring of each population will compete against the offspring of all other populations, which use different set of parameters. The effectiveness of proposed algorithm is demonstrated by phase unwrapping examples and advantages of the proposed method are discussed.

  10. Reactive power and voltage control based on general quantum genetic algorithms

    DEFF Research Database (Denmark)

    Vlachogiannis, Ioannis (John); Østergaard, Jacob

    2009-01-01

    This paper presents an improved evolutionary algorithm based on quantum computing for optima l steady-state performance of power systems. However, the proposed general quantum genetic algorithm (GQ-GA) can be applied in various combinatorial optimization problems. In this study the GQ-GA determin...

  11. Optimization of the Compensation of a Meshed MV Network by a Modified Genetic Algorithm

    DEFF Research Database (Denmark)

    Nielsen, Hans; Paar, M.; Toman, P.

    2007-01-01

    The article discusses the utilization of a modified genetic algorithm (GA) for the optimization of the shunt compensation in meshed and radial MV distribution networks. The algorithm looks for minimum costs of the network power losses and minimum capital and operating costs of applied capacitors...

  12. Machine Learning for Information Retrieval: Neural Networks, Symbolic Learning, and Genetic Algorithms.

    Science.gov (United States)

    Chen, Hsinchun

    1995-01-01

    Presents an overview of artificial-intelligence-based inductive learning techniques and their use in information science research. Three methods are discussed: the connectionist Hopfield network; the symbolic ID3/ID5R; evolution-based genetic algorithms. The knowledge representations and algorithms of these methods are examined in the context of…

  13. A hybrid Genetic and Simulated Annealing Algorithm for Chordal Ring implementation in large-scale networks

    DEFF Research Database (Denmark)

    Riaz, M. Tahir; Gutierrez Lopez, Jose Manuel; Pedersen, Jens Myrup

    2011-01-01

    of the networks. There have been many use of evolutionary algorithms to solve the problems which are in combinatory complexity nature, and extremely hard to solve by exact approaches. Both Genetic and Simulated annealing algorithms are similar in using controlled stochastic method to search the solution...

  14. Design Genetic Algorithm Optimization Education Software Based Fuzzy Controller for a Tricopter Fly Path Planning

    Science.gov (United States)

    Tran, Huu-Khoa; Chiou, Juing -Shian; Peng, Shou-Tao

    2016-01-01

    In this paper, the feasibility of a Genetic Algorithm Optimization (GAO) education software based Fuzzy Logic Controller (GAO-FLC) for simulating the flight motion control of Unmanned Aerial Vehicles (UAVs) is designed. The generated flight trajectories integrate the optimized Scaling Factors (SF) fuzzy controller gains by using GAO algorithm. The…

  15. Automated Test Assembly for Cognitive Diagnosis Models Using a Genetic Algorithm

    Science.gov (United States)

    Finkelman, Matthew; Kim, Wonsuk; Roussos, Louis A.

    2009-01-01

    Much recent psychometric literature has focused on cognitive diagnosis models (CDMs), a promising class of instruments used to measure the strengths and weaknesses of examinees. This article introduces a genetic algorithm to perform automated test assembly alongside CDMs. The algorithm is flexible in that it can be applied whether the goal is to…

  16. Synthesis of optimal digital shapers with arbitrary noise using a genetic algorithm

    Energy Technology Data Exchange (ETDEWEB)

    Regadío, Alberto, E-mail: regadioca@inta.es [Department of Computer Engineering, Space Research Group, Universidad de Alcalá, 28805 Alcalá de Henares (Spain); Electronic Technology Area, Instituto Nacional de Técnica Aeroespacial, 28850 Torrejón de Ardoz (Spain); Sánchez-Prieto, Sebastián, E-mail: sebastian.sanchez@uah.es [Department of Computer Engineering, Space Research Group, Universidad de Alcalá, 28805 Alcalá de Henares (Spain); Tabero, Jesús, E-mail: taberogj@inta.es [Electronic Technology Area, Instituto Nacional de Técnica Aeroespacial, 28850 Torrejón de Ardoz (Spain); González-Castaño, Diego M., E-mail: diego.gonzalez@usc.es [Radiation Physics Laboratory, Universidad de Santiago, 15782 Santiago de Compostela (Spain)

    2015-09-21

    This paper presents structure, design and implementation of a novel technique for determining the optimal shaping, in time-domain, for spectrometers by means of a Genetic Algorithm (GA) specifically designed for this purpose. The proposed algorithm is able to adjust automatically the coefficients for shaping an input signal. Results of this experiment have been compared to a previous simulated annealing algorithm. Finally, its performance and capabilities were tested using simulation data and a real particle detector, as a scintillator.

  17. Genetic Bee Colony (GBC) algorithm: A new gene selection method for microarray cancer classification.

    Science.gov (United States)

    Alshamlan, Hala M; Badr, Ghada H; Alohali, Yousef A

    2015-06-01

    Naturally inspired evolutionary algorithms prove effectiveness when used for solving feature selection and classification problems. Artificial Bee Colony (ABC) is a relatively new swarm intelligence method. In this paper, we propose a new hybrid gene selection method, namely Genetic Bee Colony (GBC) algorithm. The proposed algorithm combines the used of a Genetic Algorithm (GA) along with Artificial Bee Colony (ABC) algorithm. The goal is to integrate the advantages of both algorithms. The proposed algorithm is applied to a microarray gene expression profile in order to select the most predictive and informative genes for cancer classification. In order to test the accuracy performance of the proposed algorithm, extensive experiments were conducted. Three binary microarray datasets are use, which include: colon, leukemia, and lung. In addition, another three multi-class microarray datasets are used, which are: SRBCT, lymphoma, and leukemia. Results of the GBC algorithm are compared with our recently proposed technique: mRMR when combined with the Artificial Bee Colony algorithm (mRMR-ABC). We also compared the combination of mRMR with GA (mRMR-GA) and Particle Swarm Optimization (mRMR-PSO) algorithms. In addition, we compared the GBC algorithm with other related algorithms that have been recently published in the literature, using all benchmark datasets. The GBC algorithm shows superior performance as it achieved the highest classification accuracy along with the lowest average number of selected genes. This proves that the GBC algorithm is a promising approach for solving the gene selection problem in both binary and multi-class cancer classification. Copyright © 2015 Elsevier Ltd. All rights reserved.

  18. Series Hybrid Electric Vehicle Power System Optimization Based on Genetic Algorithm

    Science.gov (United States)

    Zhu, Tianjun; Li, Bin; Zong, Changfu; Wu, Yang

    2017-09-01

    Hybrid electric vehicles (HEV), compared with conventional vehicles, have complex structures and more component parameters. If variables optimization designs are carried on all these parameters, it will increase the difficulty and the convergence of algorithm program, so this paper chooses the parameters which has a major influence on the vehicle fuel consumption to make it all work at maximum efficiency. First, HEV powertrain components modelling are built. Second, taking a tandem hybrid structure as an example, genetic algorithm is used in this paper to optimize fuel consumption and emissions. Simulation results in ADVISOR verify the feasibility of the proposed genetic optimization algorithm.

  19. Use of Genetic Algorithms for Contrast and Entropy Optimization in ISAR Autofocusing

    Directory of Open Access Journals (Sweden)

    Martorella Marco

    2006-01-01

    Full Text Available Image contrast maximization and entropy minimization are two commonly used techniques for ISAR image autofocusing. When the signal phase history due to the target radial motion has to be approximated with high order polynomial models, classic optimization techniques fail when attempting to either maximize the image contrast or minimize the image entropy. In this paper a solution of this problem is proposed by using genetic algorithms. The performances of the new algorithms that make use of genetic algorithms overcome the problem with previous implementations based on deterministic approaches. Tests on real data of airplanes and ships confirm the insight.

  20. Optimal recombination in genetic algorithms for combinatorial optimization problems: Part II

    Directory of Open Access Journals (Sweden)

    Eremeev Anton V.

    2014-01-01

    Full Text Available This paper surveys results on complexity of the optimal recombination problem (ORP, which consists in finding the best possible offspring as a result of a recombination operator in a genetic algorithm, given two parent solutions. In Part II, we consider the computational complexity of ORPs arising in genetic algorithms for problems on permutations: the Travelling Salesman Problem, the Shortest Hamilton Path Problem and the Makespan Minimization on Single Machine and some other related problems. The analysis indicates that the corresponding ORPs are NP-hard, but solvable by faster algorithms, compared to the problems they are derived from.