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Sample records for genetic algorithm classifiers

  1. Road network extraction in classified SAR images using genetic algorithm

    Institute of Scientific and Technical Information of China (English)

    肖志强; 鲍光淑; 蒋晓确

    2004-01-01

    Due to the complicated background of objectives and speckle noise, it is almost impossible to extract roads directly from original synthetic aperture radar(SAR) images. A method is proposed for extraction of road network from high-resolution SAR image. Firstly, fuzzy C means is used to classify the filtered SAR image unsupervisedly, and the road pixels are isolated from the image to simplify the extraction of road network. Secondly, according to the features of roads and the membership of pixels to roads, a road model is constructed, which can reduce the extraction of road network to searching globally optimization continuous curves which pass some seed points. Finally, regarding the curves as individuals and coding a chromosome using integer code of variance relative to coordinates, the genetic operations are used to search global optimization roads. The experimental results show that the algorithm can effectively extract road network from high-resolution SAR images.

  2. 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.

  3. Genetic Algorithm with SRM SVM Classifier for Face Verification

    OpenAIRE

    Safiya K.M; Bhuvana, S.; P.TamijeSelvy; R. Radhakrishnan

    2012-01-01

    Face verification is an important problem. The problem of designing and evaluating discriminativeapproaches without explicit age modelling is used. To find the gradient orientation discard magnitudeinformation. Using hierarchical information this representation can be further improved which results inthe use of gradient orientation pyramid. When combined with a structural risk minimization support vectormachine with genetic algorithm, gradient orientation pyramid demonstrate excellent per...

  4. Text Classification using Association Rule with a Hybrid Concept of Naive Bayes Classifier and Genetic Algorithm

    CERN Document Server

    Kamruzzaman, S M; Hasan, Ahmed Ryadh

    2010-01-01

    Text classification is the automated assignment of natural language texts to predefined categories based on their content. Text classification is the primary requirement of text retrieval systems, which retrieve texts in response to a user query, and text understanding systems, which transform text in some way such as producing summaries, answering questions or extracting data. Now a day the demand of text classification is increasing tremendously. Keeping this demand into consideration, new and updated techniques are being developed for the purpose of automated text classification. This paper presents a new algorithm for text classification. Instead of using words, word relation i.e. association rules is used to derive feature set from pre-classified text documents. The concept of Naive Bayes Classifier is then used on derived features and finally a concept of Genetic Algorithm has been added for final classification. A system based on the proposed algorithm has been implemented and tested. The experimental ...

  5. Comparison of Genetic Algorithm, Particle Swarm Optimization and Biogeography-based Optimization for Feature Selection to Classify Clusters of Microcalcifications

    Science.gov (United States)

    Khehra, Baljit Singh; Pharwaha, Amar Partap Singh

    2016-06-01

    Ductal carcinoma in situ (DCIS) is one type of breast cancer. Clusters of microcalcifications (MCCs) are symptoms of DCIS that are recognized by mammography. Selection of robust features vector is the process of selecting an optimal subset of features from a large number of available features in a given problem domain after the feature extraction and before any classification scheme. Feature selection reduces the feature space that improves the performance of classifier and decreases the computational burden imposed by using many features on classifier. Selection of an optimal subset of features from a large number of available features in a given problem domain is a difficult search problem. For n features, the total numbers of possible subsets of features are 2n. Thus, selection of an optimal subset of features problem belongs to the category of NP-hard problems. In this paper, an attempt is made to find the optimal subset of MCCs features from all possible subsets of features using genetic algorithm (GA), particle swarm optimization (PSO) and biogeography-based optimization (BBO). For simulation, a total of 380 benign and malignant MCCs samples have been selected from mammogram images of DDSM database. A total of 50 features extracted from benign and malignant MCCs samples are used in this study. In these algorithms, fitness function is correct classification rate of classifier. Support vector machine is used as a classifier. From experimental results, it is also observed that the performance of PSO-based and BBO-based algorithms to select an optimal subset of features for classifying MCCs as benign or malignant is better as compared to GA-based algorithm.

  6. 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.

  7. Feature selection for outcome prediction in oesophageal cancer using genetic algorithm and random forest classifier.

    Science.gov (United States)

    Paul, Desbordes; Su, Ruan; Romain, Modzelewski; Sébastien, Vauclin; Pierre, Vera; Isabelle, Gardin

    2016-12-28

    The outcome prediction of patients can greatly help to personalize cancer treatment. A large amount of quantitative features (clinical exams, imaging, …) are potentially useful to assess the patient outcome. The challenge is to choose the most predictive subset of features. In this paper, we propose a new feature selection strategy called GARF (genetic algorithm based on random forest) extracted from positron emission tomography (PET) images and clinical data. The most relevant features, predictive of the therapeutic response or which are prognoses of the patient survival 3 years after the end of treatment, were selected using GARF on a cohort of 65 patients with a local advanced oesophageal cancer eligible for chemo-radiation therapy. The most relevant predictive results were obtained with a subset of 9 features leading to a random forest misclassification rate of 18±4% and an areas under the of receiver operating characteristic (ROC) curves (AUC) of 0.823±0.032. The most relevant prognostic results were obtained with 8 features leading to an error rate of 20±7% and an AUC of 0.750±0.108. Both predictive and prognostic results show better performances using GARF than using 4 other studied methods.

  8. 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

  9. Improving the efficacy of ERP-based BCIs using different modalities of covert visuospatial attention and a genetic algorithm-based classifier.

    Directory of Open Access Journals (Sweden)

    Mauro Marchetti

    Full Text Available We investigated whether the covert orienting of visuospatial attention can be effectively used in a brain-computer interface guided by event-related potentials. Three visual interfaces were tested: one interface that activated voluntary orienting of visuospatial attention and two interfaces that elicited automatic orienting of visuospatial attention. We used two epoch classification procedures. The online epoch classification was performed via Independent Component Analysis, and then it was followed by fixed features extraction and support vector machines classification. The offline epoch classification was performed by means of a genetic algorithm that permitted us to retrieve the relevant features of the signal, and then to categorise the features with a logistic classifier. The offline classification, but not the online one, allowed us to differentiate between the performances of the interface that required voluntary orienting of visuospatial attention and those that required automatic orienting of visuospatial attention. The offline classification revealed an advantage of the participants in using the "voluntary" interface. This advantage was further supported, for the first time, by neurophysiological data. Moreover, epoch analysis was performed better with the "genetic algorithm classifier" than with the "independent component analysis classifier". We suggest that the combined use of voluntary orienting of visuospatial attention and of a classifier that permits feature extraction ad personam (i.e., genetic algorithm classifier can lead to a more efficient control of visual BCIs.

  10. Improving the efficacy of ERP-based BCIs using different modalities of covert visuospatial attention and a genetic algorithm-based classifier.

    Science.gov (United States)

    Marchetti, Mauro; Onorati, Francesco; Matteucci, Matteo; Mainardi, Luca; Piccione, Francesco; Silvoni, Stefano; Priftis, Konstantinos

    2013-01-01

    We investigated whether the covert orienting of visuospatial attention can be effectively used in a brain-computer interface guided by event-related potentials. Three visual interfaces were tested: one interface that activated voluntary orienting of visuospatial attention and two interfaces that elicited automatic orienting of visuospatial attention. We used two epoch classification procedures. The online epoch classification was performed via Independent Component Analysis, and then it was followed by fixed features extraction and support vector machines classification. The offline epoch classification was performed by means of a genetic algorithm that permitted us to retrieve the relevant features of the signal, and then to categorise the features with a logistic classifier. The offline classification, but not the online one, allowed us to differentiate between the performances of the interface that required voluntary orienting of visuospatial attention and those that required automatic orienting of visuospatial attention. The offline classification revealed an advantage of the participants in using the "voluntary" interface. This advantage was further supported, for the first time, by neurophysiological data. Moreover, epoch analysis was performed better with the "genetic algorithm classifier" than with the "independent component analysis classifier". We suggest that the combined use of voluntary orienting of visuospatial attention and of a classifier that permits feature extraction ad personam (i.e., genetic algorithm classifier) can lead to a more efficient control of visual BCIs.

  11. Improving the Efficacy of ERP-Based BCIs Using Different Modalities of Covert Visuospatial Attention and a Genetic Algorithm-Based Classifier

    Science.gov (United States)

    Marchetti, Mauro; Onorati, Francesco; Matteucci, Matteo; Mainardi, Luca; Piccione, Francesco; Silvoni, Stefano; Priftis, Konstantinos

    2013-01-01

    We investigated whether the covert orienting of visuospatial attention can be effectively used in a brain-computer interface guided by event-related potentials. Three visual interfaces were tested: one interface that activated voluntary orienting of visuospatial attention and two interfaces that elicited automatic orienting of visuospatial attention. We used two epoch classification procedures. The online epoch classification was performed via Independent Component Analysis, and then it was followed by fixed features extraction and support vector machines classification. The offline epoch classification was performed by means of a genetic algorithm that permitted us to retrieve the relevant features of the signal, and then to categorise the features with a logistic classifier. The offline classification, but not the online one, allowed us to differentiate between the performances of the interface that required voluntary orienting of visuospatial attention and those that required automatic orienting of visuospatial attention. The offline classification revealed an advantage of the participants in using the “voluntary” interface. This advantage was further supported, for the first time, by neurophysiological data. Moreover, epoch analysis was performed better with the “genetic algorithm classifier” than with the “independent component analysis classifier”. We suggest that the combined use of voluntary orienting of visuospatial attention and of a classifier that permits feature extraction ad personam (i.e., genetic algorithm classifier) can lead to a more efficient control of visual BCIs. PMID:23342043

  12. Genetic fuzzy classifier for sleep stage identification.

    Science.gov (United States)

    Jo, Han G; Park, Jin Y; Lee, Chung K; An, Suk K; Yoo, Sun K

    2010-07-01

    Soft-computing techniques are commonly used to detect medical phenomena and help with clinical diagnoses and treatment. In this work, we propose a design for a computerized sleep scoring method, which is based on a fuzzy classifier and a genetic algorithm (GA). We design the fuzzy classifier based on the GA using a single electroencephalogram (EEG) signal that detects differences in spectral features. Polysomnography was performed on four healthy young adults (males with a mean age of 27.5 years). The sleep classifier was designed using a sleep record and tested on the sleep records of the subjects. Our results show that the genetic fuzzy classifier (GFC) agreed with visual sleep staging approximately 84.6% of the time in detection of wakefulness (WA), shallow sleep (SS), deep sleep (DS), and rapid eye movement (REM) stages.

  13. Classifier optimization method using niche genetic algorithm%基于小生境遗传算法的分类优化方法

    Institute of Scientific and Technical Information of China (English)

    李隽颖; 楼晓俊

    2012-01-01

    对于多分类问题,大多是经二分类器组合进行训练的,在分类类别多、特征维数高时,存在识别准确率不高和训练速度较慢的问题.将超球支持向量机应用到多类问题,为每个类建立一个超球体模型,通过多个超球体划分样本空间.采用改进的基于排挤的小生境遗传算法(improved crowding niche genetic algorithm,ICNGA)进行特征选择,为不同的目标类别寻找最优的特征子集,优化超球支持向量机的输入.利用UCI标准数据集的数值实验表明,在分类数据类别较多、特征维数较高时,经过ICNGA特征选择之后的多超球支持向量机的识别准确度更好,非常适合解决类别数多、特征维数高的分类问题.%According to multi-classify problem, the multi-classes classifier constructed by binary classes classifier are usually very slow to be trained. When a large number of categories of data are to be classified, the training work could be very difficult. Hyper-sphere support vector machine (HSSVM) can be extended to solve this multi-classification problem. Each category data trains only one HSSVM, the sample space is divided by multiple optimal hyper-spheres. In order to improve the performance of classifier, this paper used improved crowding niche genetic algorithm (ICNGA) to select features, chose optimal feature subset for different target classes. Using UCI data set of numerical experiment shows that the classifiers have a higher accuracy if ICNGA has been used for feature selection, especially the sample data has a large number of categories or feature vectors.

  14. A Sequential Algorithm for Training Text Classifiers

    CERN Document Server

    Lewis, D D; Lewis, David D.; Gale, William A.

    1994-01-01

    The ability to cheaply train text classifiers is critical to their use in information retrieval, content analysis, natural language processing, and other tasks involving data which is partly or fully textual. An algorithm for sequential sampling during machine learning of statistical classifiers was developed and tested on a newswire text categorization task. This method, which we call uncertainty sampling, reduced by as much as 500-fold the amount of training data that would have to be manually classified to achieve a given level of effectiveness.

  15. 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.

  16. Research on Neural Network Classifier of Target Based on Genetic-backpropagation Algorithm%基于遗传BP算法的神经网络目标分类器的研究

    Institute of Scientific and Technical Information of China (English)

    王菲; 曾庆军; 黄国建; 李洪瑞

    2001-01-01

    The development and system composition of underwater target recognition system is expounded at first, and then a novel method for training neural network target classifier by using genetic-backpropagation algorithm is proposed. The result of experiment shows that the performance of neural network target classifier based on genetic-backpropagation algorithm is better than that of neural network target classifier based on the improved backpropagation algorithm.%首先阐述了水下目标识别的研究发展和系统组成,然后提出了一种基于遗传BP算法训练神经网络目标分类器的新方法。实验结果表明采用新方法的神经网络分类器比采用改进BP算法的神经网络分类器具有更优的分类效果。

  17. Deterministic Pattern Classifier Based on Genetic Programming

    Institute of Scientific and Technical Information of China (English)

    LI Jian-wu; LI Min-qiang; KOU Ji-song

    2001-01-01

    This paper proposes a supervised training-test method with Genetic Programming (GP) for pattern classification. Compared and contrasted with traditional methods with regard to deterministic pattern classifiers, this method is true for both linear separable problems and linear non-separable problems. For specific training samples, it can formulate the expression of discriminate function well without any prior knowledge. At last, an experiment is conducted, and the result reveals that this system is effective and practical.

  18. [Optimizing algorithm design of piecewise linear classifier for spectra].

    Science.gov (United States)

    Lan, Tian-Ge; Fang, Yong-Hua; Xiong, Wei; Kong, Chao; Li, Da-Cheng; Dong, Da-Ming

    2008-11-01

    Being able to identify pollutant gases quickly and accurately is a basic request of spectroscopic technique for envirment monitoring for spectral classifier. Piecewise linear classifier is simple needs less computational time and approachs nonlinear boundary beautifully. Combining piecewise linear classifier and linear support vector machine which is based on the principle of maximizing margin, an optimizing algorithm for single side piecewise linear classifier was devised. Experimental results indicate that the piecewise linear classifier trained by the optimizing algorithm proposed in this paper can approach nonolinear boundary with fewer super_planes and has higher veracity for classification and recognition.

  19. 基于混沌遗传算法的模糊LS-SVM分类器及其应用%Fuzzy LS-SVM Classifier Based on Chaos Genetic Algorithm and Its Application

    Institute of Scientific and Technical Information of China (English)

    王禾军; 邓飞其; 陈治明

    2011-01-01

    为克服支持向量机算法对噪声点和异常点的敏感性,采用清晰集合构造模糊集合法确定隶属度,采用混沌遗传算法优化参数的模糊最小二乘支持向量机分类器(FLS-SVMBCGA),并用著名的Ripley数据集、MONK数据集和PIMA数据集进行了数值实验,对油气输送管道的TPD检测信号进行了诊断.结果表明,FLS-SVMBCGA分类器能有效提高带噪声点和异常点数据集分类的预测精度,对油气输送管道的TPD信号分类效果高于91.67%,可实现对油气输送管道TPD信号的准确诊断.%In order to reduce the sensitivity of the support vector machines (SVM) to noise and outliers, a new fuzzy least squares-support vector machines classifier based on chaos genetic algorithm is proposed and is abbreviated to FLS-SVMBCGA, in which the clear sets are used to construct a fuzzy membership set and the chaos genetic algorithm is adopted to optimize the parameters. Then, some experiments are carried ont on three benchmarking datasets such as the Ripley dataset, the MONK dataset and the PIMA dataset. Finally, the TPD signals from oil and gas transmission pipeline are diagnosed using the proposed classifier. The results show that FLS-SVMBCGA is effective in improving the prediction accuracy of the classification problems with noises or outliers, with a classifying effect for TPD signals being higher than 91.67%, which means that the proposed algorithm can accurately diagnose the TPD signals from oil and gas transmission pipeline.

  20. A Multiple Classifier Fusion Algorithm Using Weighted Decision Templates

    Directory of Open Access Journals (Sweden)

    Aizhong Mi

    2016-01-01

    Full Text Available Fusing classifiers’ decisions can improve the performance of a pattern recognition system. Many applications areas have adopted the methods of multiple classifier fusion to increase the classification accuracy in the recognition process. From fully considering the classifier performance differences and the training sample information, a multiple classifier fusion algorithm using weighted decision templates is proposed in this paper. The algorithm uses a statistical vector to measure the classifier’s performance and makes a weighed transform on each classifier according to the reliability of its output. To make a decision, the information in the training samples around an input sample is used by the k-nearest-neighbor rule if the algorithm evaluates the sample as being highly likely to be misclassified. An experimental comparison was performed on 15 data sets from the KDD’99, UCI, and ELENA databases. The experimental results indicate that the algorithm can achieve better classification performance. Next, the algorithm was applied to cataract grading in the cataract ultrasonic phacoemulsification operation. The application result indicates that the proposed algorithm is effective and can meet the practical requirements of the operation.

  1. Comparision of Clustering Algorithms usingNeural Network Classifier for Satellite Image Classification

    Directory of Open Access Journals (Sweden)

    S.Praveena

    2015-06-01

    Full Text Available This paper presents a hybrid clustering algorithm and feed-forward neural network classifier for land-cover mapping of trees, shade, building and road. It starts with the single step preprocessing procedure to make the image suitable for segmentation. The pre-processed image is segmented using the hybrid genetic-Artificial Bee Colony(ABC algorithm that is developed by hybridizing the ABC and FCM to obtain the effective segmentation in satellite image and classified using neural network . The performance of the proposed hybrid algorithm is compared with the algorithms like, k-means, Fuzzy C means(FCM, Moving K-means, Artificial Bee Colony(ABC algorithm, ABC-GA algorithm, Moving KFCM and KFCM algorithm.

  2. Genetic Algorithms and Local Search

    Science.gov (United States)

    Whitley, Darrell

    1996-01-01

    The first part of this presentation is a tutorial level introduction to the principles of genetic search and models of simple genetic algorithms. The second half covers the combination of genetic algorithms with local search methods to produce hybrid genetic algorithms. Hybrid algorithms can be modeled within the existing theoretical framework developed for simple genetic algorithms. An application of a hybrid to geometric model matching is given. The hybrid algorithm yields results that improve on the current state-of-the-art for this problem.

  3. 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.

  4. Filter selection using genetic algorithms

    Science.gov (United States)

    Patel, Devesh

    1996-03-01

    Convolution operators act as matched filters for certain types of variations found in images and have been extensively used in the analysis of images. However, filtering through a bank of N filters generates N filtered images, consequently increasing the amount of data considerably. Moreover, not all these filters have the same discriminatory capabilities for the individual images, thus making the task of any classifier difficult. In this paper, we use genetic algorithms to select a subset of relevant filters. Genetic algorithms represent a class of adaptive search techniques where the processes are similar to natural selection of biological evolution. The steady state model (GENITOR) has been used in this paper. The reduction of filters improves the performance of the classifier (which in this paper is the multi-layer perceptron neural network) and furthermore reduces the computational requirement. In this study we use the Laws filters which were proposed for the analysis of texture images. Our aim is to recognize the different textures on the images using the reduced filter set.

  5. Web Based Genetic Algorithm Using Data Mining

    OpenAIRE

    Ashiqur Rahman; Asaduzzaman Noman; Md. Ashraful Islam; Al-Amin Gaji

    2016-01-01

    This paper presents an approach for classifying students in order to predict their final grade based on features extracted from logged data in an education web-based system. A combination of multiple classifiers leads to a significant improvement in classification performance. Through weighting the feature vectors using a Genetic Algorithm we can optimize the prediction accuracy and get a marked improvement over raw classification. It further shows that when the number of features is few; fea...

  6. 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.

  7. Genetic Programming and Genetic Algorithms for Propositions

    Directory of Open Access Journals (Sweden)

    Nabil M. HEWAHI

    2012-01-01

    Full Text Available In this paper we propose a mechanism to discover the compound proposition solutions for a given truth table without knowing the compound propositions that lead to the truth table results. The approach is based on two proposed algorithms, the first is called Producing Formula (PF algorithm which is based on the genetic programming idea, to find out the compound proposition solutions for the given truth table. The second algorithm is called the Solutions Optimization (SO algorithm which is based on genetic algorithms idea, to find a list of the optimum compound propositions that can solve the truth table. The obtained list will depend on the solutions obtained from the PF algorithm. Various types of genetic operators have been introduced to obtain the solutions either within the PF algorithm or SO algorithm.

  8. 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.

  9. Multi-input distributed classifiers for synthetic genetic circuits.

    Directory of Open Access Journals (Sweden)

    Oleg Kanakov

    Full Text Available For practical construction of complex synthetic genetic networks able to perform elaborate functions it is important to have a pool of relatively simple modules with different functionality which can be compounded together. To complement engineering of very different existing synthetic genetic devices such as switches, oscillators or logical gates, we propose and develop here a design of synthetic multi-input classifier based on a recently introduced distributed classifier concept. A heterogeneous population of cells acts as a single classifier, whose output is obtained by summarizing the outputs of individual cells. The learning ability is achieved by pruning the population, instead of tuning parameters of an individual cell. The present paper is focused on evaluating two possible schemes of multi-input gene classifier circuits. We demonstrate their suitability for implementing a multi-input distributed classifier capable of separating data which are inseparable for single-input classifiers, and characterize performance of the classifiers by analytical and numerical results. The simpler scheme implements a linear classifier in a single cell and is targeted at separable classification problems with simple class borders. A hard learning strategy is used to train a distributed classifier by removing from the population any cell answering incorrectly to at least one training example. The other scheme implements a circuit with a bell-shaped response in a single cell to allow potentially arbitrary shape of the classification border in the input space of a distributed classifier. Inseparable classification problems are addressed using soft learning strategy, characterized by probabilistic decision to keep or discard a cell at each training iteration. We expect that our classifier design contributes to the development of robust and predictable synthetic biosensors, which have the potential to affect applications in a lot of fields, including that of

  10. Multi-input distributed classifiers for synthetic genetic circuits.

    Science.gov (United States)

    Kanakov, Oleg; Kotelnikov, Roman; Alsaedi, Ahmed; Tsimring, Lev; Huerta, Ramón; Zaikin, Alexey; Ivanchenko, Mikhail

    2015-01-01

    For practical construction of complex synthetic genetic networks able to perform elaborate functions it is important to have a pool of relatively simple modules with different functionality which can be compounded together. To complement engineering of very different existing synthetic genetic devices such as switches, oscillators or logical gates, we propose and develop here a design of synthetic multi-input classifier based on a recently introduced distributed classifier concept. A heterogeneous population of cells acts as a single classifier, whose output is obtained by summarizing the outputs of individual cells. The learning ability is achieved by pruning the population, instead of tuning parameters of an individual cell. The present paper is focused on evaluating two possible schemes of multi-input gene classifier circuits. We demonstrate their suitability for implementing a multi-input distributed classifier capable of separating data which are inseparable for single-input classifiers, and characterize performance of the classifiers by analytical and numerical results. The simpler scheme implements a linear classifier in a single cell and is targeted at separable classification problems with simple class borders. A hard learning strategy is used to train a distributed classifier by removing from the population any cell answering incorrectly to at least one training example. The other scheme implements a circuit with a bell-shaped response in a single cell to allow potentially arbitrary shape of the classification border in the input space of a distributed classifier. Inseparable classification problems are addressed using soft learning strategy, characterized by probabilistic decision to keep or discard a cell at each training iteration. We expect that our classifier design contributes to the development of robust and predictable synthetic biosensors, which have the potential to affect applications in a lot of fields, including that of medicine and industry.

  11. Genetic algorithm optimization of entanglement

    CERN Document Server

    Navarro-Munoz, J C; Rosu, H C; Navarro-Munoz, Jorge C.

    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

  12. Boosting Principal Component Analysis by Genetic Algorithm

    Directory of Open Access Journals (Sweden)

    Divya Somvanshi

    2010-07-01

    Full Text Available This paper presents a new method of feature extraction by combining principal component analysis and genetic algorithm. Use of multiple pre-processors in combination with principal component analysis generates alternate feature spaces for data representation. The present method works out the fusion of these multiple spaces to create higher dimensionality feature vectors. The fused feature vectors are given chromosome representation by taking feature components to be genes. Then these feature vectors are allowed to undergo genetic evolution individually. For genetic algorithm, initial population is created by calculating probability distance matrix, and by applying a probability distance metric such that all the genes which lie farther than a defined threshold are tripped to zero. The genetic evolution of fused feature vector brings out most significant feature components (genes as survivours. A measure of significance is adapted on the basis of frequency of occurrence of the surviving genes in the current population. Finally, the feature vector is obtained by weighting the original feature components in proportion to their significance. The present algorithm is validated in combination with a neural network classifier based on error backpropagation algorithm, and by analysing a number of benchmark datasets available in the open sources.Defence Science Journal, 2010, 60(4, pp.392-398, DOI:http://dx.doi.org/10.14429/dsj.60.495

  13. Voice Matching Using Genetic Algorithm

    Directory of Open Access Journals (Sweden)

    Abhishek Bal

    2014-03-01

    Full Text Available In this paper, the use of Genetic Algorithm (GA for voice recognition is described. The practical application of Genetic Algorithm (GA to the solution of engineering problem is a rapidly emerging approach in the field of control engineering and signal processing. Genetic algorithms are useful for searching a space in multi-directional way from large spaces and poorly defined space. Voice is a signal of infinite information. Digital processing of voice signal is very important for automatic voice recognition technology. Nowadays, voice processing is very much important in security mechanism due to mimicry characteristic. So studying the voice feature extraction in voice processing is very necessary in military, hospital, telephone system, investigation bureau and etc. In order to extract valuable information from the voice signal, make decisions on the process, and obtain results, the data needs to be manipulated and analyzed. In this paper, if the instant voice is not matched with same person’s reference voices in the database, then Genetic Algorithm (GA is applied between two randomly chosen reference voices. Again the instant voice is compared with the result of Genetic Algorithm (GA which is used, including its three main steps: selection, crossover and mutation. We illustrate our approach with different sample of voices from human in our institution.

  14. 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.

  15. 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.

  16. Function Optimization Based on Quantum Genetic Algorithm

    OpenAIRE

    Ying Sun; Hegen Xiong

    2014-01-01

    Optimization method is important in engineering design and application. Quantum genetic algorithm has the characteristics of good population diversity, rapid convergence and good global search capability and so on. It combines quantum algorithm with genetic algorithm. A novel quantum genetic algorithm is proposed, which is called Variable-boundary-coded Quantum Genetic Algorithm (vbQGA) in which qubit chromosomes are collapsed into variable-boundary-coded chromosomes instead of binary-coded c...

  17. Function Optimization Based on Quantum Genetic Algorithm

    OpenAIRE

    Ying Sun; Yuesheng Gu; Hegen Xiong

    2013-01-01

    Quantum genetic algorithm has the characteristics of good population diversity, rapid convergence and good global search capability and so on.It combines quantum algorithm with genetic algorithm. A novel quantum genetic algorithm is proposed ,which is called variable-boundary-coded quantum genetic algorithm (vbQGA) in which qubit chromosomes are collapsed into variableboundary- coded chromosomes instead of binary-coded chromosomes. Therefore much shorter chromosome strings can be gained.The m...

  18. Genetic algorithms for protein threading.

    Science.gov (United States)

    Yadgari, J; Amir, A; Unger, R

    1998-01-01

    Despite many years of efforts, a direct prediction of protein structure from sequence is still not possible. As a result, in the last few years researchers have started to address the "inverse folding problem": Identifying and aligning a sequence to the fold with which it is most compatible, a process known as "threading". In two meetings in which protein folding predictions were objectively evaluated, it became clear that threading as a concept promises a real breakthrough, but that much improvement is still needed in the technique itself. Threading is a NP-hard problem, and thus no general polynomial solution can be expected. Still a practical approach with demonstrated ability to find optimal solutions in many cases, and acceptable solutions in other cases, is needed. We applied the technique of Genetic Algorithms in order to significantly improve the ability of threading algorithms to find the optimal alignment of a sequence to a structure, i.e. the alignment with the minimum free energy. A major progress reported here is the design of a representation of the threading alignment as a string of fixed length. With this representation validation of alignments and genetic operators are effectively implemented. Appropriate data structure and parameters have been selected. It is shown that Genetic Algorithm threading is effective and is able to find the optimal alignment in a few test cases. Furthermore, the described algorithm is shown to perform well even without pre-definition of core elements. Existing threading methods are dependent on such constraints to make their calculations feasible. But the concept of core elements is inherently arbitrary and should be avoided if possible. While a rigorous proof is hard to submit yet an, we present indications that indeed Genetic Algorithm threading is capable of finding consistently good solutions of full alignments in search spaces of size up to 10(70).

  19. Results of Evolution Supervised by Genetic Algorithms

    CERN Document Server

    Jäntschi, Lorentz; Bălan, Mugur C; Sestraş, Radu E

    2010-01-01

    A series of results of evolution supervised by genetic algorithms with interest to agricultural and horticultural fields are reviewed. New obtained original results from the use of genetic algorithms on structure-activity relationships are reported.

  20. Improved Genetic Algorithm Application in Textile Defect Detection

    Institute of Scientific and Technical Information of China (English)

    GENG Zhao-feng; Li Bei-bei; ZHAO Zhi-hong

    2007-01-01

    Based on an efficient improved genetic algorithm,a pattern recognition approach is represented for textile defects inspection. An image process is developed to automatically detect the drawbacks on textile caused by three circumstances: break, dual, and jump of yams. By statistic method, some texture feature values of the image with defects points can be achieved. Therefore, the textile defects are classified properly. The advanced process of the defect image is done. Image segmentation is realized by an improved genetic algorithm to detect the defects. This method can be used to automatically classify and detect textile defects. According to different users' requirements, ifferent types of textile material can be detected.

  1. Nurse Rostering with Genetic Algorithms

    CERN Document Server

    Aickelin, Uwe

    2010-01-01

    In recent years genetic algorithms have emerged as a useful tool for the heuristic solution of complex discrete optimisation problems. In particular there has been considerable interest in their use in tackling problems arising in the areas of scheduling and timetabling. However, the classical genetic algorithm paradigm is not well equipped to handle constraints and successful implementations usually require some sort of modification to enable the search to exploit problem specific knowledge in order to overcome this shortcoming. This paper is concerned with the development of a family of genetic algorithms for the solution of a nurse rostering problem at a major UK hospital. The hospital is made up of wards of up to 30 nurses. Each ward has its own group of nurses whose shifts have to be scheduled on a weekly basis. In addition to fulfilling the minimum demand for staff over three daily shifts, nurses' wishes and qualifications have to be taken into account. The schedules must also be seen to be fair, in tha...

  2. Protein fold classification with genetic algorithms and feature selection.

    Science.gov (United States)

    Chen, Peng; Liu, Chunmei; Burge, Legand; Mahmood, Mohammad; Southerland, William; Gloster, Clay

    2009-10-01

    Protein fold classification is a key step to predicting protein tertiary structures. This paper proposes a novel approach based on genetic algorithms and feature selection to classifying protein folds. Our dataset is divided into a training dataset and a test dataset. Each individual for the genetic algorithms represents a selection function of the feature vectors of the training dataset. A support vector machine is applied to each individual to evaluate the fitness value (fold classification rate) of each individual. The aim of the genetic algorithms is to search for the best individual that produces the highest fold classification rate. The best individual is then applied to the feature vectors of the test dataset and a support vector machine is built to classify protein folds based on selected features. Our experimental results on Ding and Dubchak's benchmark dataset of 27-class folds show that our approach achieves an accuracy of 71.28%, which outperforms current state-of-the-art protein fold predictors.

  3. 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.

  4. Improved genetic operator for genetic algorithm

    Institute of Scientific and Technical Information of China (English)

    林峰; 杨启文

    2002-01-01

    The mutation operator has been seldom improved because researchers ha rdly suspect its ability to prevent genetic algorithm (GA) from converging prema turely. Due to its i mportance to GA, the authors of this paper study its influence on the diversity of genes in the same locus, and point out that traditional mutation, to some ext ent, can result in premature convergence of genes (PCG) in the same locus. The a bove drawback of the traditional mutation operator causes the loss of critical a lleles. Inspired by digital technique, we introduce two kinds of boolean operati on into GA to develop a novel mutation operator and discuss its contribution to preventing the loss of critical alleles. The experimental results of function op timization show that the improved mutation operator can effectively prevent prem ature convergence, and can provide a wide selection range of control parameters for GA.

  5. Improved genetic operator for genetic algorithm

    Institute of Scientific and Technical Information of China (English)

    林峰; 杨启文

    2002-01-01

    The mutation operator has been seldom improved because ressearchers hardly suspect its ability to prevent genetic algorithm(GA) from converging prematurely.Due to its importance to GA,the authors of this paper study influence on the diversity of genes in the same locus,and point out that traditional mutation,to some extent,can result in premature convergence of genes(PCG) in the same locus.The above drawback of the traditional mutation operator causes the loss of critical alleles.Inspired by digital technique,we introduce two kinds of boolean operation into GA to develop a novel mutation operator and discuss its contribution of preventing the loss of critical alleles.The experimental results of function optimizatioin show that the improved mutation operator can effectively prevent premature convegence,and can provide a wide selection range of control parameters for GA.

  6. Classifying algorithms for SIFT-MS technology and medical diagnosis.

    Science.gov (United States)

    Moorhead, K T; Lee, D; Chase, J G; Moot, A R; Ledingham, K M; Scotter, J; Allardyce, R A; Senthilmohan, S T; Endre, Z

    2008-03-01

    Selected Ion Flow Tube-Mass Spectrometry (SIFT-MS) is an analytical technique for real-time quantification of trace gases in air or breath samples. SIFT-MS system thus offers unique potential for early, rapid detection of disease states. Identification of volatile organic compound (VOC) masses that contribute strongly towards a successful classification clearly highlights potential new biomarkers. A method utilising kernel density estimates is thus presented for classifying unknown samples. It is validated in a simple known case and a clinical setting before-after dialysis. The simple case with nitrogen in Tedlar bags returned a 100% success rate, as expected. The clinical proof-of-concept with seven tests on one patient had an ROC curve area of 0.89. These results validate the method presented and illustrate the emerging clinical potential of this technology.

  7. 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…

  8. 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…

  9. Incremental multiple objective genetic algorithms.

    Science.gov (United States)

    Chen, Qian; Guan, Sheng-Uei

    2004-06-01

    This paper presents a new genetic algorithm approach to multiobjective optimization problems--incremental multiple objective genetic algorithms (IMOGA). Different from conventional MOGA methods, it takes each objective into consideration incrementally. The whole evolution is divided into as many phases as the number of objectives, and one more objective is considered in each phase. Each phase is composed of two stages. First, an independent population is evolved to optimize one specific objective. Second, the better-performing individuals from the single-objecive population evolved in the above stage and the multiobjective population evolved in the last phase are joined together by the operation of integration. The resulting population then becomes an initial multiobjective population, to which a multiobjective evolution based on the incremented objective set is applied. The experiment results show that, in most problems, the performance of IMOGA is better than that of three other MOGAs, NSGA-II, SPEA, and PAES. IMOGA can find more solutions during the same time span, and the quality of solutions is better.

  10. Excursion-Set-Mediated Genetic Algorithm

    Science.gov (United States)

    Noever, David; Baskaran, Subbiah

    1995-01-01

    Excursion-set-mediated genetic algorithm (ESMGA) is embodiment of method of searching for and optimizing computerized mathematical models. Incorporates powerful search and optimization techniques based on concepts analogous to natural selection and laws of genetics. In comparison with other genetic algorithms, this one achieves stronger condition for implicit parallelism. Includes three stages of operations in each cycle, analogous to biological generation.

  11. Excursion-Set-Mediated Genetic Algorithm

    Science.gov (United States)

    Noever, David; Baskaran, Subbiah

    1995-01-01

    Excursion-set-mediated genetic algorithm (ESMGA) is embodiment of method of searching for and optimizing computerized mathematical models. Incorporates powerful search and optimization techniques based on concepts analogous to natural selection and laws of genetics. In comparison with other genetic algorithms, this one achieves stronger condition for implicit parallelism. Includes three stages of operations in each cycle, analogous to biological generation.

  12. A Genetic Algorithm-Based Feature Selection

    Directory of Open Access Journals (Sweden)

    Babatunde Oluleye

    2014-07-01

    Full Text Available This article details the exploration and application of Genetic Algorithm (GA for feature selection. Particularly a binary GA was used for dimensionality reduction to enhance the performance of the concerned classifiers. In this work, hundred (100 features were extracted from set of images found in the Flavia dataset (a publicly available dataset. The extracted features are Zernike Moments (ZM, Fourier Descriptors (FD, Lengendre Moments (LM, Hu 7 Moments (Hu7M, Texture Properties (TP and Geometrical Properties (GP. The main contributions of this article are (1 detailed documentation of the GA Toolbox in MATLAB and (2 the development of a GA-based feature selector using a novel fitness function (kNN-based classification error which enabled the GA to obtain a combinatorial set of feature giving rise to optimal accuracy. The results obtained were compared with various feature selectors from WEKA software and obtained better results in many ways than WEKA feature selectors in terms of classification accuracy

  13. Genetic Algorithms: Basic Concept and Applications

    Directory of Open Access Journals (Sweden)

    Ms. Amninder Kaur

    2013-07-01

    Full Text Available Genetic algorithms are a part of evolutionary computing, which is a rapidly growing area of artificial intelligence. Genetic algorithms have been applied to a wide range of practical problems often with valuable results. Genetic algorithms are often viewed as function optimizer, although the range of problems to which genetic algorithms have been applied are quite broad. This paper covers the basic concepts of genetic algorithms and their applications to a variety of fields. It also tries to give a solution to the problem of economic load dispatch using Genetic Algorithms. An attempt has been made to explain when and why GA should be used as an optimization tool. Finally, the paper points to future directions

  14. 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...

  15. Application of Chaos in Genetic Algorithms

    Institute of Scientific and Technical Information of China (English)

    YANG Li-Jiang; CHEN Tian-Lun

    2002-01-01

    Through replacing Gaussian mutation operator in real-coded genetic algorithm with a chaotic mapping, wepresent a genetic algorithm with chaotic mutation. To examine this new algorithm, we applied our algorithm to functionoptimization problems and obtained good results. Furthermore the orbital points' distribution of chaotic mapping andthe effects of chaotic mutation with different parameters were studied in order to make the chaotic mutation mechanismbe utilized efficiently.

  16. Biomimetic use of genetic algorithms

    CERN Document Server

    Dessalles, Jean-Louis

    2011-01-01

    Genetic algorithms are considered as an original way to solve problems, probably because of their generality and of their "blind" nature. But GAs are also unusual since the features of many implementations (among all that could be thought of) are principally led by the biological metaphor, while efficiency measurements intervene only afterwards. We propose here to examine the relevance of these biomimetic aspects, by pointing out some fundamental similarities and divergences between GAs and the genome of living beings shaped by natural selection. One of the main differences comes from the fact that GAs rely principally on the so-called implicit parallelism, while giving to the mutation/selection mechanism the second role. Such differences could suggest new ways of employing GAs on complex problems, using complex codings and starting from nearly homogeneous populations.

  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. Structure and weights optimisation of a modified Elman network emotion classifier using hybrid computational intelligence algorithms: a comparative study

    Science.gov (United States)

    Sheikhan, Mansour; Abbasnezhad Arabi, Mahdi; Gharavian, Davood

    2015-10-01

    Artificial neural networks are efficient models in pattern recognition applications, but their performance is dependent on employing suitable structure and connection weights. This study used a hybrid method for obtaining the optimal weight set and architecture of a recurrent neural emotion classifier based on gravitational search algorithm (GSA) and its binary version (BGSA), respectively. By considering the features of speech signal that were related to prosody, voice quality, and spectrum, a rich feature set was constructed. To select more efficient features, a fast feature selection method was employed. The performance of the proposed hybrid GSA-BGSA method was compared with similar hybrid methods based on particle swarm optimisation (PSO) algorithm and its binary version, PSO and discrete firefly algorithm, and hybrid of error back-propagation and genetic algorithm that were used for optimisation. Experimental tests on Berlin emotional database demonstrated the superior performance of the proposed method using a lighter network structure.

  19. Web Based Genetic Algorithm Using Data Mining

    Directory of Open Access Journals (Sweden)

    Ashiqur Rahman

    2016-09-01

    Full Text Available This paper presents an approach for classifying students in order to predict their final grade based on features extracted from logged data in an education web-based system. A combination of multiple classifiers leads to a significant improvement in classification performance. Through weighting the feature vectors using a Genetic Algorithm we can optimize the prediction accuracy and get a marked improvement over raw classification. It further shows that when the number of features is few; feature weighting is works better than just feature selection. Many leading educational institutions are working to establish an online teaching and learning presence. Several systems with different capabilities and approaches have been developed to deliver online education in an academic setting. In particular, Michigan State University (MSU has pioneered some of these systems to provide an infrastructure for online instruction. The research presented here was performed on a part of the latest online educational system developed at MSU, the Learning Online Network with Computer-Assisted Personalized Approach (LON-CAPA

  20. Genetic algorithms in seasonal demand forecasting

    OpenAIRE

    Chodak, Grzegorz; Kwaśnicki, Witold

    2000-01-01

    The method of forecasting seasonal demand applying genetic algorithm is presented. Specific form of used demand function is shown in the first section of the article. Next the method of identification of the function parameters using genetic algorithms is discussed. In the final section an example of applying proposed method to forecast real demand process is shown.

  1. Dynamic Route Guidance Using Improved Genetic Algorithms

    Directory of Open Access Journals (Sweden)

    Zhanke Yu

    2013-01-01

    Full Text Available This paper presents an improved genetic algorithm (IGA for dynamic route guidance algorithm. The proposed IGA design a vicinity crossover technique and a greedy backward mutation technique to increase the population diversity and strengthen local search ability. The steady-state reproduction is introduced to protect the optimized genetic individuals. Furthermore the junction delay is introduced to the fitness function. The simulation results show the effectiveness of the proposed algorithm.

  2. Genetic algorithms for route discovery.

    Science.gov (United States)

    Gelenbe, Erol; Liu, Peixiang; Lainé, Jeremy

    2006-12-01

    Packet routing in networks requires knowledge about available paths, which can be either acquired dynamically while the traffic is being forwarded, or statically (in advance) based on prior information of a network's topology. This paper describes an experimental investigation of path discovery using genetic algorithms (GAs). We start with the quality-of-service (QoS)-driven routing protocol called "cognitive packet network" (CPN), which uses smart packets (SPs) to dynamically select routes in a distributed autonomic manner based on a user's QoS requirements. We extend it by introducing a GA at the source routers, which modifies and filters the paths discovered by the CPN. The GA can combine the paths that were previously discovered to create new untested but valid source-to-destination paths, which are then selected on the basis of their "fitness." We present an implementation of this approach, where the GA runs in background mode so as not to overload the ingress routers. Measurements conducted on a network test bed indicate that when the background-traffic load of the network is light to medium, the GA can result in improved QoS. When the background-traffic load is high, it appears that the use of the GA may be detrimental to the QoS experienced by users as compared to CPN routing because the GA uses less timely state information in its decision making.

  3. Predicting Protein Structure Using Parallel Genetic Algorithms.

    Science.gov (United States)

    1994-12-01

    34 IEEE Transactions on Systems, Man and Cybernetics, 10(9) (September 1980). 16. De Jong, Kenneth A. "On Using Genetic Algoriths to Search Program...By " Predicting rotein Structure D istribticfiar.. ................ Using Parallel Genetic Algorithms ,Avaiu " ’ •"... Dist THESIS I IGeorge H...iiLite-d Approved for public release; distribution unlimited AFIT/ GCS /ENG/94D-03 Predicting Protein Structure Using Parallel Genetic Algorithms

  4. A GREEDY GENETIC ALGORITHM FOR UNCONSTRAINED GLOBAL OPTIMIZATION

    Institute of Scientific and Technical Information of China (English)

    ZHAO Xinchao

    2005-01-01

    The greedy algorithm is a strong local searching algorithm. The genetica lgorithm is generally applied to the global optimization problems. In this paper, we combine the greedy idea and the genetic algorithm to propose the greedy genetic algorithm which incorporates the global exploring ability of the genetic algorithm and the local convergent ability of the greedy algorithm. Experimental results show that greedy genetic algorithm gives much better results than the classical genetic algorithm.

  5. 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.

  6. Genetic Algorithm for Solving Simple Mathematical Equality Problem

    OpenAIRE

    Hermawanto, Denny

    2013-01-01

    This paper explains genetic algorithm for novice in this field. Basic philosophy of genetic algorithm and its flowchart are described. Step by step numerical computation of genetic algorithm for solving simple mathematical equality problem will be briefly explained

  7. Solving Maximal Clique Problem through Genetic Algorithm

    Science.gov (United States)

    Rajawat, Shalini; Hemrajani, Naveen; Menghani, Ekta

    2010-11-01

    Genetic algorithm is one of the most interesting heuristic search techniques. It depends basically on three operations; selection, crossover and mutation. The outcome of the three operations is a new population for the next generation. Repeating these operations until the termination condition is reached. All the operations in the algorithm are accessible with today's molecular biotechnology. The simulations show that with this new computing algorithm, it is possible to get a solution from a very small initial data pool, avoiding enumerating all candidate solutions. For randomly generated problems, genetic algorithm can give correct solution within a few cycles at high probability.

  8. Function Optimization Based on Quantum Genetic Algorithm

    Directory of Open Access Journals (Sweden)

    Ying Sun

    2014-01-01

    Full Text Available Optimization method is important in engineering design and application. Quantum genetic algorithm has the characteristics of good population diversity, rapid convergence and good global search capability and so on. It combines quantum algorithm with genetic algorithm. A novel quantum genetic algorithm is proposed, which is called Variable-boundary-coded Quantum Genetic Algorithm (vbQGA in which qubit chromosomes are collapsed into variable-boundary-coded chromosomes instead of binary-coded chromosomes. Therefore much shorter chromosome strings can be gained. The method of encoding and decoding of chromosome is first described before a new adaptive selection scheme for angle parameters used for rotation gate is put forward based on the core ideas and principles of quantum computation. Eight typical functions are selected to optimize to evaluate the effectiveness and performance of vbQGA against standard Genetic Algorithm (sGA and Genetic Quantum Algorithm (GQA. The simulation results show that vbQGA is significantly superior to sGA in all aspects and outperforms GQA in robustness and solving velocity, especially for multidimensional and complicated functions.

  9. 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.

  10. Hybrid Genetic Algorithms with Fuzzy Logic Controller

    Institute of Scientific and Technical Information of China (English)

    2001-01-01

    In this paper, a new implementation of genetic algorithms (GAs) is developed for the machine scheduling problem, which is abundant among the modern manufacturing systems. The performance measure of early and tardy completion of jobs is very natural as one's aim, which is usually to minimize simultaneously both earliness and tardiness of all jobs. As the problem is NP-hard and no effective algorithms exist, we propose a hybrid genetic algorithms approach to deal with it. We adjust the crossover and mutation probabilities by fuzzy logic controller whereas the hybrid genetic algorithm does not require preliminary experiments to determine probabilities for genetic operators. The experimental results show the effectiveness of the GAs method proposed in the paper.``

  11. 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...

  12. Genetic Algorithms, Floating Point Numbers and Applications

    Science.gov (United States)

    Hardy, Yorick; Steeb, Willi-Hans; Stoop, Ruedi

    The core in most genetic algorithms is the bitwise manipulations of bit strings. We show that one can directly manipulate the bits in floating point numbers. This means the main bitwise operations in genetic algorithm mutations and crossings are directly done inside the floating point number. Thus the interval under consideration does not need to be known in advance. For applications, we consider the roots of polynomials and finding solutions of linear equations.

  13. Quantum Genetic Algorithms for Computer Scientists

    OpenAIRE

    Rafael Lahoz-Beltra

    2016-01-01

    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 Geneti...

  14. The Application of a Dendritic Cell Algorithm to a Robotic Classifier

    CERN Document Server

    Oates, Robert; Aickelin, Uwe; Garibaldi, Jonathan M; Kendall, Graham

    2010-01-01

    The dendritic cell algorithm is an immune-inspired technique for processing time-dependant data. Here we propose it as a possible solution for a robotic classification problem. The dendritic cell algorithm is implemented on a real robot and an investigation is performed into the effects of varying the migration threshold median for the cell population. The algorithm performs well on a classification task with very little tuning. Ways of extending the implementation to allow it to be used as a classifier within the field of robotic security are suggested.

  15. An algorithm for generating modular hierarchical neural network classifiers: a step toward larger scale applications

    Science.gov (United States)

    Roverso, Davide

    2003-08-01

    Many-class learning is the problem of training a classifier to discriminate among a large number of target classes. Together with the problem of dealing with high-dimensional patterns (i.e. a high-dimensional input space), the many class problem (i.e. a high-dimensional output space) is a major obstacle to be faced when scaling-up classifier systems and algorithms from small pilot applications to large full-scale applications. The Autonomous Recursive Task Decomposition (ARTD) algorithm is here proposed as a solution to the problem of many-class learning. Example applications of ARTD to neural classifier training are also presented. In these examples, improvements in training time are shown to range from 4-fold to more than 30-fold in pattern classification tasks of both static and dynamic character.

  16. A HYBRID CLASSIFICATION ALGORITHM TO CLASSIFY ENGINEERING STUDENTS’ PROBLEMS AND PERKS

    Directory of Open Access Journals (Sweden)

    Mitali Desai

    2016-03-01

    Full Text Available The social networking sites have brought a new horizon for expressing views and opinions of individuals. Moreover, they provide medium to students to share their sentiments including struggles and joy during the learning process. Such informal information has a great venue for decision making. The large and growing scale of information needs automatic classification techniques. Sentiment analysis is one of the automated techniques to classify large data. The existing predictive sentiment analysis techniques are highly used to classify reviews on E-commerce sites to provide business intelligence. However, they are not much useful to draw decisions in education system since they classify the sentiments into merely three pre-set categories: positive, negative and neutral. Moreover, classifying the students’ sentiments into positive or negative category does not provide deeper insight into their problems and perks. In this paper, we propose a novel Hybrid Classification Algorithm to classify engineering students’ sentiments. Unlike traditional predictive sentiment analysis techniques, the proposed algorithm makes sentiment analysis process descriptive. Moreover, it classifies engineering students’ perks in addition to problems into several categories to help future students and education system in decision making.

  17. Analog Module Placement Design Using Genetic Algorithm

    Institute of Scientific and Technical Information of China (English)

    2003-01-01

    This paper presents a novel genetic algorithm for analog module placement based on ageneralization of the two-dimensional bin packing problem. The genetic encoding and operators assure that allproblem constraints are always satisfied. Thus the potential problems of adding penalty terms to the costfunction are eliminated so that the search configuration space is drastically decreased. The dedicated costfunction is based on the special requirements of analog integrated circuits. A fractional factorial experimentwas conducted using an orthogonal array to study the algorithm parameters. A meta GA was applied todetermine the optimal parameter values. The algorithm was tested with several local benchmark circuits. Theexperimental results show that the algorithm has better performance than the simulated annealing approachwith satisfactory results comparable to manual placement. This study demonstrates the effectiveness of thegenetic algorithm in the analog module placement problem. The algorithm has been successfully used in alayout synthesis tool.

  18. Evolving evolutionary algorithms using linear genetic programming.

    Science.gov (United States)

    Oltean, Mihai

    2005-01-01

    A new model for evolving Evolutionary Algorithms is proposed in this paper. The model is based on the Linear Genetic Programming (LGP) technique. Every LGP chromosome encodes an EA which is used for solving a particular problem. Several Evolutionary Algorithms for function optimization, the Traveling Salesman Problem and the Quadratic Assignment Problem are evolved by using the considered model. Numerical experiments show that the evolved Evolutionary Algorithms perform similarly and sometimes even better than standard approaches for several well-known benchmarking problems.

  19. A New Fuzzy Adaptive Genetic Algorithm

    Institute of Scientific and Technical Information of China (English)

    FANG Lei; ZHANG Huan-chun; JING Ya-zhi

    2005-01-01

    Multiple genetic algorithms (GAs) need a large population size, which will take a long time for evolution.A new fuzzy adaptive GA is proposed in this paper. This algorithm is more effective in global search while keeping the overall population size constant. The simulation results of function optimization show that with the proposed algorithm, the phenomenon of premature convergence can be overcome effectively, and a satisfying optimization result is obtained.

  20. Robot path planning using genetic algorithms

    Institute of Scientific and Technical Information of China (English)

    2001-01-01

    Presents a strategy for soccer robot path planning using genetic algorithms for which, real number coding method is used, to overcome the defects of binary coding method, and the double crossover operation a dopted, to avoid the common defect of early convergence and converge faster than the standard genetic algo rithms concludes from simulation results that the method is effective for robot path planning.

  1. Adaptive Genetic Algorithm Model for Intrusion Detection

    Directory of Open Access Journals (Sweden)

    K. S. Anil Kumar

    2012-09-01

    Full Text Available Intrusion detection systems are intelligent systems designed to identify and prevent the misuse of computer networks and systems. Various approaches to Intrusion Detection are currently being used, but they are relatively ineffective. Thus the emerging network security systems need be part of the life system and this ispossible only by embedding knowledge into the network. The Adaptive Genetic Algorithm Model - IDS comprising of K-Means clustering Algorithm, Genetic Algorithm and Neural Network techniques. Thetechnique is tested using multitude of background knowledge sets in DARPA network traffic datasets.

  2. Genetic Algorithms for multiple objective vehicle routing

    CERN Document Server

    Geiger, Martin Josef

    2008-01-01

    The talk describes a general approach of a genetic algorithm for multiple objective optimization problems. A particular dominance relation between the individuals of the population is used to define a fitness operator, enabling the genetic algorithm to adress even problems with efficient, but convex-dominated alternatives. The algorithm is implemented in a multilingual computer program, solving vehicle routing problems with time windows under multiple objectives. The graphical user interface of the program shows the progress of the genetic algorithm and the main parameters of the approach can be easily modified. In addition to that, the program provides powerful decision support to the decision maker. The software has proved it's excellence at the finals of the European Academic Software Award EASA, held at the Keble college/ University of Oxford/ Great Britain.

  3. 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.

  4. Demarcation of potential seismic sources on integration of genetic algorithm and BP algorithm

    Institute of Scientific and Technical Information of China (English)

    周庆; 叶洪

    2002-01-01

    In this paper potential seismic sources in coastal region of South China are identified by integration of genetic algorithm (GA) and back propagation (BP algorithm). GA is used for finding the best parameter combination rapidly in an infinite solution space for artificial neural networks (ANN). The results show that the distribution of potential seismic sources with different upper magnitude demarcated by this classifier is mostly satisfied the intrinsic relationship between seismic environment and earthquake occurrence, with less effect from subjective judgment of human being.

  5. Interactive Genetic Algorithms with Fitness Adjustment

    Institute of Scientific and Technical Information of China (English)

    GUO Guang-song; GONG Dun-wei; HAO Guo-sheng; ZHANG Yong

    2006-01-01

    Noises widely exist in interactive genetic algorithms. However, there is no effective method to solve this problem up to now. There are two kinds of noises, one is the noise existing in visual systems and the other is resulted from user's preference mechanisms. Characteristics of the two noises are presented aiming at the application of interactive genetic algorithms in dealing with images. The evolutionary phases of interactive genetic algorithms are determined according to differences in the same individual's fitness among different generations. Models for noises in different phases are established and the corresponding strategies for reducing noises are given. The algorithm proposed in this paper has been applied to fashion design, which is a typical example of image processing. The results show that the strategies can reduce noises in interactive genetic algorithms and improve the algorithm's performance effectively. However, a further study is needed to solve the problem of determining the evolution phase by using suitable objective methods so as to find out an effective method to decrease noises.

  6. Combinatorial Multiobjective Optimization Using Genetic Algorithms

    Science.gov (United States)

    Crossley, William A.; Martin. Eric T.

    2002-01-01

    The research proposed in this document investigated multiobjective optimization approaches based upon the Genetic Algorithm (GA). Several versions of the GA have been adopted for multiobjective design, but, prior to this research, there had not been significant comparisons of the most popular strategies. The research effort first generalized the two-branch tournament genetic algorithm in to an N-branch genetic algorithm, then the N-branch GA was compared with a version of the popular Multi-Objective Genetic Algorithm (MOGA). Because the genetic algorithm is well suited to combinatorial (mixed discrete / continuous) optimization problems, the GA can be used in the conceptual phase of design to combine selection (discrete variable) and sizing (continuous variable) tasks. Using a multiobjective formulation for the design of a 50-passenger aircraft to meet the competing objectives of minimizing takeoff gross weight and minimizing trip time, the GA generated a range of tradeoff designs that illustrate which aircraft features change from a low-weight, slow trip-time aircraft design to a heavy-weight, short trip-time aircraft design. Given the objective formulation and analysis methods used, the results of this study identify where turboprop-powered aircraft and turbofan-powered aircraft become more desirable for the 50 seat passenger application. This aircraft design application also begins to suggest how a combinatorial multiobjective optimization technique could be used to assist in the design of morphing aircraft.

  7. Feature selection for optimized skin tumor recognition using genetic algorithms.

    Science.gov (United States)

    Handels, H; Ross, T; Kreusch, J; Wolff, H H; Pöppl, S J

    1999-07-01

    In this paper, a new approach to computer supported diagnosis of skin tumors in dermatology is presented. High resolution skin surface profiles are analyzed to recognize malignant melanomas and nevocytic nevi (moles), automatically. In the first step, several types of features are extracted by 2D image analysis methods characterizing the structure of skin surface profiles: texture features based on cooccurrence matrices, Fourier features and fractal features. Then, feature selection algorithms are applied to determine suitable feature subsets for the recognition process. Feature selection is described as an optimization problem and several approaches including heuristic strategies, greedy and genetic algorithms are compared. As quality measure for feature subsets, the classification rate of the nearest neighbor classifier computed with the leaving-one-out method is used. Genetic algorithms show the best results. Finally, neural networks with error back-propagation as learning paradigm are trained using the selected feature sets. Different network topologies, learning parameters and pruning algorithms are investigated to optimize the classification performance of the neural classifiers. With the optimized recognition system a classification performance of 97.7% is achieved.

  8. 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.

  9. Multicast Routing Based on Hybrid Genetic Algorithm

    Institute of Scientific and Technical Information of China (English)

    CAO Yuan-da; CAI Gui

    2005-01-01

    A new multicast routing algorithm based on the hybrid genetic algorithm (HGA) is proposed. The coding pattern based on the number of routing paths is used. A fitness function that is computed easily and makes algorithm quickly convergent is proposed. A new approach that defines the HGA's parameters is provided. The simulation shows that the approach can increase largely the convergent ratio, and the fitting values of the parameters of this algorithm are different from that of the original algorithms. The optimal mutation probability of HGA equals 0.50 in HGA in the experiment, but that equals 0.07 in SGA. It has been concluded that the population size has a significant influence on the HGA's convergent ratio when it's mutation probability is bigger. The algorithm with a small population size has a high average convergent rate. The population size has little influence on HGA with the lower mutation probability.

  10. Genetic Algorithm Approaches for Actuator Placement

    Science.gov (United States)

    Crossley, William A.

    2000-01-01

    This research investigated genetic algorithm approaches for smart actuator placement to provide aircraft maneuverability without requiring hinged flaps or other control surfaces. The effort supported goals of the Multidisciplinary Design Optimization focus efforts in NASA's Aircraft au program. This work helped to properly identify various aspects of the genetic algorithm operators and parameters that allow for placement of discrete control actuators/effectors. An improved problem definition, including better definition of the objective function and constraints, resulted from this research effort. The work conducted for this research used a geometrically simple wing model; however, an increasing number of potential actuator placement locations were incorporated to illustrate the ability of the GA to determine promising actuator placement arrangements. This effort's major result is a useful genetic algorithm-based approach to assist in the discrete actuator/effector placement problem.

  11. Classifying spatially heterogeneous wetland communities using machine learning algorithms and spectral and textural features.

    Science.gov (United States)

    Szantoi, Zoltan; Escobedo, Francisco J; Abd-Elrahman, Amr; Pearlstine, Leonard; Dewitt, Bon; Smith, Scot

    2015-05-01

    Mapping of wetlands (marsh vs. swamp vs. upland) is a common remote sensing application.Yet, discriminating between similar freshwater communities such as graminoid/sedge fromremotely sensed imagery is more difficult. Most of this activity has been performed using medium to low resolution imagery. There are only a few studies using highspatial resolutionimagery and machine learning image classification algorithms for mapping heterogeneouswetland plantcommunities. This study addresses this void by analyzing whether machine learning classifierssuch as decisiontrees (DT) and artificial neural networks (ANN) can accurately classify graminoid/sedgecommunities usinghigh resolution aerial imagery and image texture data in the Everglades National Park, Florida.In addition tospectral bands, the normalized difference vegetation index, and first- and second-order texturefeatures derivedfrom the near-infrared band were analyzed. Classifier accuracies were assessed using confusiontablesand the calculated kappa coefficients of the resulting maps. The results indicated that an ANN(multilayerperceptron based on backpropagation) algorithm produced a statistically significantly higheraccuracy(82.04%) than the DT (QUEST) algorithm (80.48%) or the maximum likelihood (80.56%)classifier (α<0.05). Findings show that using multiple window sizes provided the best results. First-ordertexture featuresalso provided computational advantages and results that were not significantly different fromthose usingsecond-order texture features.

  12. Using genetic algorithms to select and create features for pattern classification. Technical report

    Energy Technology Data Exchange (ETDEWEB)

    Chang, E.I.; Lippmann, R.P.

    1991-03-11

    Genetic algorithms were used to select and create features and to select reference exemplar patterns for machine vision and speech pattern classification tasks. On a 15-feature machine-vision inspection task, it was found that genetic algorithms performed no better than conventional approaches to feature selection but required much more computation. For a speech recognition task, genetic algorithms required no more computation time than traditional approaches but reduced the number of features required by a factor of five (from 153 to 33 features). On a difficult artificial machine-vision task, genetic algorithms were able to create new features (polynomial functions of the original features) that reduced classification error rates from 10 to almost 0 percent. Neural net and nearest-neighbor classifiers were unable to provide such low error rates using only the original features. Genetic algorithms were also used to reduce the number of reference exemplar patterns and to select the value of k for a k-nearest-neighbor classifier. On a .338 training pattern vowel recognition problem with 10 classes, genetic algorithms simultaneously reduced the number of stored exemplars from 338 to 63 and selected k without significantly decreasing classification accuracy. In all applications, genetic algorithms were easy to apply and found good solutions in many fewer trials than would be required by an exhaustive search. Run times were long but not unreasonable. These results suggest that genetic algorithms may soon be practical for pattern classification problems as faster serial and parallel computers are developed.

  13. 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.

  14. 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.

  15. A simple algorithm for optimization and model fitting: AGA (asexual genetic algorithm)

    Science.gov (United States)

    Cantó, J.; Curiel, S.; Martínez-Gómez, E.

    2009-07-01

    Context: Mathematical optimization can be used as a computational tool to obtain the optimal solution to a given problem in a systematic and efficient way. For example, in twice-differentiable functions and problems with no constraints, the optimization consists of finding the points where the gradient of the objective function is zero and using the Hessian matrix to classify the type of each point. Sometimes, however it is impossible to compute these derivatives and other type of techniques must be employed such as the steepest descent/ascent method and more sophisticated methods such as those based on the evolutionary algorithms. Aims: We present a simple algorithm based on the idea of genetic algorithms (GA) for optimization. We refer to this algorithm as AGA (asexual genetic algorithm) and apply it to two kinds of problems: the maximization of a function where classical methods fail and model fitting in astronomy. For the latter case, we minimize the chi-square function to estimate the parameters in two examples: the orbits of exoplanets by taking a set of radial velocity data, and the spectral energy distribution (SED) observed towards a YSO (Young Stellar Object). Methods: The algorithm AGA may also be called genetic, although it differs from standard genetic algorithms in two main aspects: a) the initial population is not encoded; and b) the new generations are constructed by asexual reproduction. Results: Applying our algorithm in optimizing some complicated functions, we find the global maxima within a few iterations. For model fitting to the orbits of exoplanets and the SED of a YSO, we estimate the parameters and their associated errors.

  16. Genetic Algorithms for Digital Quantum Simulations.

    Science.gov (United States)

    Las Heras, U; Alvarez-Rodriguez, U; Solano, E; Sanz, M

    2016-06-10

    We propose genetic algorithms, which are robust optimization techniques inspired by natural selection, to enhance the versatility of digital quantum simulations. In this sense, we show that genetic algorithms can be employed to increase the fidelity and optimize the resource requirements of digital quantum simulation protocols while adapting naturally to the experimental constraints. Furthermore, this method allows us to reduce not only digital errors but also experimental errors in quantum gates. Indeed, by adding ancillary qubits, we design a modular gate made out of imperfect gates, whose fidelity is larger than the fidelity of any of the constituent gates. Finally, we prove that the proposed modular gates are resilient against different gate errors.

  17. Applying a Genetic Algorithm to Reconfigurable Hardware

    Science.gov (United States)

    Wells, B. Earl; Weir, John; Trevino, Luis; Patrick, Clint; Steincamp, Jim

    2004-01-01

    This paper investigates the feasibility of applying genetic algorithms to solve optimization problems that are implemented entirely in reconfgurable hardware. The paper highlights the pe$ormance/design space trade-offs that must be understood to effectively implement a standard genetic algorithm within a modem Field Programmable Gate Array, FPGA, reconfgurable hardware environment and presents a case-study where this stochastic search technique is applied to standard test-case problems taken from the technical literature. In this research, the targeted FPGA-based platform and high-level design environment was the Starbridge Hypercomputing platform, which incorporates multiple Xilinx Virtex II FPGAs, and the Viva TM graphical hardware description language.

  18. Genetic Algorithms for Digital Quantum Simulations

    Science.gov (United States)

    Las Heras, U.; Alvarez-Rodriguez, U.; Solano, E.; Sanz, M.

    2016-06-01

    We propose genetic algorithms, which are robust optimization techniques inspired by natural selection, to enhance the versatility of digital quantum simulations. In this sense, we show that genetic algorithms can be employed to increase the fidelity and optimize the resource requirements of digital quantum simulation protocols while adapting naturally to the experimental constraints. Furthermore, this method allows us to reduce not only digital errors but also experimental errors in quantum gates. Indeed, by adding ancillary qubits, we design a modular gate made out of imperfect gates, whose fidelity is larger than the fidelity of any of the constituent gates. Finally, we prove that the proposed modular gates are resilient against different gate errors.

  19. 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«.

  20. The Applications of Genetic Algorithms in Medicine

    Directory of Open Access Journals (Sweden)

    Ali Ghaheri

    2015-11-01

    Full Text Available 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.

  1. 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

  2. Hybrid Genetic Algorithms for University Course Timetabling

    Directory of Open Access Journals (Sweden)

    Meysam Shahvali Kohshori

    2012-03-01

    Full Text Available University course timetabling is one of the important and time consuming issues that each University is involved with it at the beginning of each. 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 near optimal feasible solutions instead of exact solutions. Genetic algorithms, because of multidirectional search property of them, 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 proposed algorithms, fuzzy logic is used to measure violation of soft constraints in fitness function to deal with inherent uncertainly 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.

  3. An Improved Fast Compressive Tracking Algorithm Based on Online Random Forest Classifier

    Directory of Open Access Journals (Sweden)

    Xiong Jintao

    2016-01-01

    Full Text Available The fast compressive tracking (FCT algorithm is a simple and efficient algorithm, which is proposed in recent years. But, it is difficult to deal with the factors such as occlusion, appearance changes, pose variation, etc in processing. The reasons are that, Firstly, even if the naive Bayes classifier is fast in training, it is not robust concerning the noise. Secondly, the parameters are required to vary with the unique environment for accurate tracking. In this paper, we propose an improved fast compressive tracking algorithm based on online random forest (FCT-ORF for robust visual tracking. Firstly, we combine ideas with the adaptive compressive sensing theory regarding the weighted random projection to exploit both local and discriminative information of the object. The second reason is the online random forest classifier for online tracking which is demonstrated with more robust to the noise adaptively and high computational efficiency. The experimental results show that the algorithm we have proposed has a better performance in the field of occlusion, appearance changes, and pose variation than the fast compressive tracking algorithm’s contribution.

  4. Cognitive radio resource allocation based on coupled chaotic genetic algorithm

    Institute of Scientific and Technical Information of China (English)

    Zu Yun-Xiao; Zhou Jie; Zeng Chang-Chang

    2010-01-01

    A coupled chaotic genetic algorithm for cognitive radio resource allocation which is based on genetic algorithm and coupled Logistic map is proposed. A fitness function for cognitive radio resource allocation is provided. Simulations are conducted for cognitive radio resource allocation by using the coupled chaotic genetic algorithm, simple genetic algorithm and dynamic allocation algorithm respectively. The simulation results show that, compared with simple genetic and dynamic allocation algorithm, coupled chaotic genetic algorithm reduces the total transmission power and bit error rate in cognitive radio system, and has faster convergence speed.

  5. Cognitive radio resource allocation based on coupled chaotic genetic algorithm

    Science.gov (United States)

    Zu, Yun-Xiao; Zhou, Jie; Zeng, Chang-Chang

    2010-11-01

    A coupled chaotic genetic algorithm for cognitive radio resource allocation which is based on genetic algorithm and coupled Logistic map is proposed. A fitness function for cognitive radio resource allocation is provided. Simulations are conducted for cognitive radio resource allocation by using the coupled chaotic genetic algorithm, simple genetic algorithm and dynamic allocation algorithm respectively. The simulation results show that, compared with simple genetic and dynamic allocation algorithm, coupled chaotic genetic algorithm reduces the total transmission power and bit error rate in cognitive radio system, and has faster convergence speed.

  6. Multiprocessor Scheduling Using Parallel Genetic Algorithm

    Directory of Open Access Journals (Sweden)

    Nourah Al-Angari

    2012-07-01

    Full Text Available Tasks scheduling is the most challenging problem in the parallel computing. Hence, the inappropriate scheduling will reduce or even abort the utilization of the true potential of the parallelization. Genetic algorithm (GA has been successfully applied to solve the scheduling problem. The fitness evaluation is the most time consuming GA operation for the CPU time, which affect the GA performance. The proposed synchronous master-slave algorithm outperforms the sequential algorithm in case of complex and high number of generations problem.

  7. Nested Genetic Algorithm for Resolving Overlapped Spectral Bands

    Institute of Scientific and Technical Information of China (English)

    2000-01-01

    A nested genetic algorithm, including genetic parameter level and genetic implemented level for peak parameters, was proposed and applied for resolving overlapped spectral bands. By the genetic parameter level, parameters of genetic algorithm were optimized; moreover, the number of overlapped peaks was determined simultaneously. Then parameters of individual peaks were computed with the genetic implemented level.

  8. MULTIOBJECTIVE PARALLEL GENETIC ALGORITHM FOR WASTE MINIMIZATION

    Science.gov (United States)

    In this research we have developed an efficient multiobjective parallel genetic algorithm (MOPGA) for waste minimization problems. This MOPGA integrates PGAPack (Levine, 1996) and NSGA-II (Deb, 2000) with novel modifications. PGAPack is a master-slave parallel implementation of a...

  9. 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.

  10. Genetic Algorithm for Chinese Postman Problems

    Institute of Scientific and Technical Information of China (English)

    Jiang Hua; Kang Li-shan

    2003-01-01

    Chinese Postman Problem is an unsettled graphic problem. It was approached seldom by evolutionary computation. Now we use genetic algorithm to solve Chinese Postman Problem in undirected graph and get good results. It could be extended to solve Chinese postman problem in directed graph. We make these efforts for exploring in optimizing the mixed Chinese postman problem.

  11. Proposed genetic algorithms for construction site layout

    NARCIS (Netherlands)

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

    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 availab

  12. Focused Crawler Optimization Using Genetic Algorithm

    Directory of Open Access Journals (Sweden)

    Hartanto Kusuma Wardana

    2011-12-01

    Full Text Available As the size of the Web continues to grow, searching it for useful information has become more difficult. Focused crawler intends to explore the Web conform to a specific topic. This paper discusses the problems caused by local searching algorithms. Crawler can be trapped within a limited Web community and overlook suitable Web pages outside its track. A genetic algorithm as a global searching algorithm is modified to address the problems. The genetic algorithm is used to optimize Web crawling and to select more suitable Web pages to be fetched by the crawler. Several evaluation experiments are conducted to examine the effectiveness of the approach. The crawler delivers collections consist of 3396 Web pages from 5390 links which had been visited, or filtering rate of Roulette-Wheel selection at 63% and precision level at 93% in 5 different categories. The result showed that the utilization of genetic algorithm had empowered focused crawler to traverse the Web comprehensively, despite it relatively small collections. Furthermore, it brought up a great potential for building an exemplary collections compared to traditional focused crawling methods.

  13. Algorithms for Image Analysis and Combination of Pattern Classifiers with Application to Medical Diagnosis

    Science.gov (United States)

    Georgiou, Harris

    2009-10-01

    Medical Informatics and the application of modern signal processing in the assistance of the diagnostic process in medical imaging is one of the more recent and active research areas today. This thesis addresses a variety of issues related to the general problem of medical image analysis, specifically in mammography, and presents a series of algorithms and design approaches for all the intermediate levels of a modern system for computer-aided diagnosis (CAD). The diagnostic problem is analyzed with a systematic approach, first defining the imaging characteristics and features that are relevant to probable pathology in mammo-grams. Next, these features are quantified and fused into new, integrated radio-logical systems that exhibit embedded digital signal processing, in order to improve the final result and minimize the radiological dose for the patient. In a higher level, special algorithms are designed for detecting and encoding these clinically interest-ing imaging features, in order to be used as input to advanced pattern classifiers and machine learning models. Finally, these approaches are extended in multi-classifier models under the scope of Game Theory and optimum collective deci-sion, in order to produce efficient solutions for combining classifiers with minimum computational costs for advanced diagnostic systems. The material covered in this thesis is related to a total of 18 published papers, 6 in scientific journals and 12 in international conferences.

  14. Support Vector Machine Ensemble Based on Genetic Algorithm

    Institute of Scientific and Technical Information of China (English)

    LI Ye; YIN Ru-po; CAI Yun-ze; XU Xiao-ming

    2006-01-01

    Support vector machines (SVMs) have been introduced as effective methods for solving classification problems.However, due to some limitations in practical applications,their generalization performance is sometimes far from the expected level. Therefore, it is meaningful to study SVM ensemble learning. In this paper, a novel genetic algorithm based ensemble learning method, namely Direct Genetic Ensemble (DGE), is proposed. DGE adopts the predictive accuracy of ensemble as the fitness function and searches a good ensemble from the ensemble space. In essence, DGE is also a selective ensemble learning method because the base classifiers of the ensemble are selected according to the solution of genetic algorithm. In comparison with other ensemble learning methods, DGE works on a higher level and is more direct. Different strategies of constructing diverse base classifiers can be utilized in DGE.Experimental results show that SVM ensembles constructed by DGE can achieve better performance than single SVMs,bagged and boosted SVM ensembles. In addition, some valuable conclusions are obtained.

  15. Evolving Quantum Circuits using Genetic Algorithms

    CERN Document Server

    Prashant

    2005-01-01

    This paper describes an application of genetic algorithm for evolving quantum computing circuits. The circuits use reversible one qubit and two qubit gates which are applied on a multi-qubit system having some initial state. The genetic algorithm automatically searches the space and comes out with the appropriate circuit design, which yields desired output state. The fitness function used matches the output with desired output and the search stops when it is found. The fitness value becomes higher if the output is close to the desired output. The paper briefly discusses the operation of a quantum gate over the multi-qubit system. The paper also demonstrates some examples of the evolved circuits using the algorithm.

  16. Stego-audio Using Genetic Algorithm Approach

    Directory of Open Access Journals (Sweden)

    V. Santhi

    2014-06-01

    Full Text Available With the rapid development of digital multimedia applications, the secure data transmission becomes the main issue in data communication system. So the multimedia data hiding techniques have been developed to ensure the secured data transfer. Steganography is an art of hiding a secret message within an image/audio/video file in such a way that the secret message cannot be perceived by hacker/intruder. In this study, we use RSA encryption algorithm to encrypt the message and Genetic Algorithm (GA to encode the message in the audio file. This study presents a method to access the negative audio bytes and includes the negative audio bytes in the message encoding and position embedding process. This increases the capacity of encoding message in the audio file. The use of GA operators in Genetic Algorithm reduces the noise distortions.

  17. Genetic algorithm optimization for finned channel performance

    Institute of Scientific and Technical Information of China (English)

    2007-01-01

    Compared to a smooth channel, a finned channel provides a higher heat transfer coefficient; increasing the fin height enhances the heat transfer. However, this heat transfer enhancement is associated with an increase in the pressure drop. This leads to an increased pumping power requirement so that one may seek an optimum design for such systems. The main goal of this paper is to define the exact location and size of fins in such a way that a minimal pressure drop coincides with an optimal heat transfer based on the genetic algorithm. Each fin arrangement is considered a solution to the problem(an individual for genetic algorithm). An initial population is generated randomly at the first step. Then the algorithm has been searched among these solutions and made new solutions iteratively by its functions to find an optimum design as reported in this article.

  18. Learning Bayesian networks using genetic algorithm

    Institute of Scientific and Technical Information of China (English)

    Chen Fei; Wang Xiufeng; Rao Yimei

    2007-01-01

    A new method to evaluate the fitness of the Bayesian networks according to the observed data is provided. The main advantage of this criterion is that it is suitable for both the complete and incomplete cases while the others not.Moreover it facilitates the computation greatly. In order to reduce the search space, the notation of equivalent class proposed by David Chickering is adopted. Instead of using the method directly, the novel criterion, variable ordering, and equivalent class are combined,moreover the proposed mthod avoids some problems caused by the previous one. Later, the genetic algorithm which allows global convergence, lack in the most of the methods searching for Bayesian network is applied to search for a good model in thisspace. To speed up the convergence, the genetic algorithm is combined with the greedy algorithm. Finally, the simulation shows the validity of the proposed approach.

  19. Spectral areas and ratios classifier algorithm for pancreatic tissue classification using optical spectroscopy.

    Science.gov (United States)

    Chandra, Malavika; Scheiman, James; Simeone, Diane; McKenna, Barbara; Purdy, Julianne; Mycek, Mary-Ann

    2010-01-01

    Pancreatic adenocarcinoma is one of the leading causes of cancer death, in part because of the inability of current diagnostic methods to reliably detect early-stage disease. We present the first assessment of the diagnostic accuracy of algorithms developed for pancreatic tissue classification using data from fiber optic probe-based bimodal optical spectroscopy, a real-time approach that would be compatible with minimally invasive diagnostic procedures for early cancer detection in the pancreas. A total of 96 fluorescence and 96 reflectance spectra are considered from 50 freshly excised tissue sites-including human pancreatic adenocarcinoma, chronic pancreatitis (inflammation), and normal tissues-on nine patients. Classification algorithms using linear discriminant analysis are developed to distinguish among tissues, and leave-one-out cross-validation is employed to assess the classifiers' performance. The spectral areas and ratios classifier (SpARC) algorithm employs a combination of reflectance and fluorescence data and has the best performance, with sensitivity, specificity, negative predictive value, and positive predictive value for correctly identifying adenocarcinoma being 85, 89, 92, and 80%, respectively.

  20. Spectral areas and ratios classifier algorithm for pancreatic tissue classification using optical spectroscopy

    Science.gov (United States)

    Chandra, Malavika; Scheiman, James; Simeone, Diane; McKenna, Barbara; Purdy, Julianne; Mycek, Mary-Ann

    2010-01-01

    Pancreatic adenocarcinoma is one of the leading causes of cancer death, in part because of the inability of current diagnostic methods to reliably detect early-stage disease. We present the first assessment of the diagnostic accuracy of algorithms developed for pancreatic tissue classification using data from fiber optic probe-based bimodal optical spectroscopy, a real-time approach that would be compatible with minimally invasive diagnostic procedures for early cancer detection in the pancreas. A total of 96 fluorescence and 96 reflectance spectra are considered from 50 freshly excised tissue sites-including human pancreatic adenocarcinoma, chronic pancreatitis (inflammation), and normal tissues-on nine patients. Classification algorithms using linear discriminant analysis are developed to distinguish among tissues, and leave-one-out cross-validation is employed to assess the classifiers' performance. The spectral areas and ratios classifier (SpARC) algorithm employs a combination of reflectance and fluorescence data and has the best performance, with sensitivity, specificity, negative predictive value, and positive predictive value for correctly identifying adenocarcinoma being 85, 89, 92, and 80%, respectively.

  1. 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.

  2. Surface/Surface Intersection Using Simulated Annealing Genetic Algorithm

    Institute of Scientific and Technical Information of China (English)

    2000-01-01

    The genetic algorithm and marching method are integrated into a novel algorithm to solve the surface intersection problem. By combining genetic algorithm with local searching method the efficiency of evolution is greatly improved. By fully utilizing the global searching ability and instinct attribute for parallel computation of genetic algorithm and the local rapid convergency of marching method, the algorithm can compute the intersection robustly and generate correct topology of intersection curves. The details of the new algorithm are discussed here.

  3. Genetic algorithms for the vehicle routing problem

    Science.gov (United States)

    Volna, Eva

    2016-06-01

    The Vehicle Routing Problem (VRP) is one of the most challenging combinatorial optimization tasks. This problem consists in designing the optimal set of routes for fleet of vehicles in order to serve a given set of customers. Evolutionary algorithms are general iterative algorithms for combinatorial optimization. These algorithms have been found to be very effective and robust in solving numerous problems from a wide range of application domains. This problem is known to be NP-hard; hence many heuristic procedures for its solution have been suggested. For such problems it is often desirable to obtain approximate solutions, so they can be found fast enough and are sufficiently accurate for the purpose. In this paper we have performed an experimental study that indicates the suitable use of genetic algorithms for the vehicle routing problem.

  4. 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.

  5. A supervised contextual classifier based on a region-growth algorithm

    DEFF Research Database (Denmark)

    Lira, Jorge; Maletti, Gabriela Mariel

    2002-01-01

    A supervised classification scheme to segment optical multi-spectral images has been developed. In this classifier, an automated region-growth algorithm delineates the training sets. This algorithm handles three parameters: an initial pixel seed, a window size and a threshold for each class....... A suitable pixel seed is manually implanted through visual inspection of the image classes. The best value for the window and the threshold are obtained from a spectral distance and heuristic criteria. This distance is calculated from a mathematical model of spectral separability. A pixel is incorporated...... into a region if a spectral homogeneity criterion is satisfied in the pixel-centered window for a given threshold. The homogeneity criterion is obtained from the model of spectral distance. The set of pixels forming a region represents a statistically valid sample of a defined class signaled by the initial...

  6. A supervised contextual classifier based on a region-growth algorithm

    DEFF Research Database (Denmark)

    Lira, Jorge; Maletti, Gabriela Mariel

    2002-01-01

    A supervised classification scheme to segment optical multi-spectral images has been developed. In this classifier, an automated region-growth algorithm delineates the training sets. This algorithm handles three parameters: an initial pixel seed, a window size and a threshold for each class...... pixel seed. The grown regions therefore constitute suitable training sets for each class. Comparing the statistical behavior of the pixel population of a sliding window with that of each class performs the classification. For region-growth, a window size is employed for each class. For classification....... A suitable pixel seed is manually implanted through visual inspection of the image classes. The best value for the window and the threshold are obtained from a spectral distance and heuristic criteria. This distance is calculated from a mathematical model of spectral separability. A pixel is incorporated...

  7. Classification of hyperspectral remote sensing images based on simulated annealing genetic algorithm and multiple instance learning

    Institute of Scientific and Technical Information of China (English)

    高红民; 周惠; 徐立中; 石爱业

    2014-01-01

    A hybrid feature selection and classification strategy was proposed based on the simulated annealing genetic algorithm and multiple instance learning (MIL). The band selection method was proposed from subspace decomposition, which combines the simulated annealing algorithm with the genetic algorithm in choosing different cross-over and mutation probabilities, as well as mutation individuals. Then MIL was combined with image segmentation, clustering and support vector machine algorithms to classify hyperspectral image. The experimental results show that this proposed method can get high classification accuracy of 93.13%at small training samples and the weaknesses of the conventional methods are overcome.

  8. A simple algorithm for optimization and model fitting: AGA (asexual genetic algorithm)

    CERN Document Server

    Canto, J; Martinez-Gomez, E; 10.1051/0004-6361/200911740

    2009-01-01

    Context. Mathematical optimization can be used as a computational tool to obtain the optimal solution to a given problem in a systematic and efficient way. For example, in twice-differentiable functions and problems with no constraints, the optimization consists of finding the points where the gradient of the objective function is zero and using the Hessian matrix to classify the type of each point. Sometimes, however it is impossible to compute these derivatives and other type of techniques must be employed such as the steepest descent/ascent method and more sophisticated methods such as those based on the evolutionary algorithms. Aims. We present a simple algorithm based on the idea of genetic algorithms (GA) for optimization. We refer to this algorithm as AGA (Asexual Genetic Algorithm) and apply it to two kinds of problems: the maximization of a function where classical methods fail and model fitting in astronomy. For the latter case, we minimize the chi-square function to estimate the parameters in two e...

  9. Niche Genetic Algorithm with Accurate Optimization Performance

    Institute of Scientific and Technical Information of China (English)

    LIU Jian-hua; YAN De-kun

    2005-01-01

    Based on crowding mechanism, a novel niche genetic algorithm was proposed which can record evolutionary direction dynamically during evolution. After evolution, the solutions's precision can be greatly improved by means of the local searching along the recorded direction. Simulation shows that this algorithm can not only keep population diversity but also find accurate solutions. Although using this method has to take more time compared with the standard GA, it is really worth applying to some cases that have to meet a demand for high solution precision.

  10. Routine Discovery of Complex Genetic Models using Genetic Algorithms.

    Science.gov (United States)

    Moore, Jason H; Hahn, Lance W; Ritchie, Marylyn D; Thornton, Tricia A; White, Bill C

    2004-02-01

    Simulation studies are useful in various disciplines for a number of reasons including the development and evaluation of new computational and statistical methods. This is particularly true in human genetics and genetic epidemiology where new analytical methods are needed for the detection and characterization of disease susceptibility genes whose effects are complex, nonlinear, and partially or solely dependent on the effects of other genes (i.e. epistasis or gene-gene interaction). Despite this need, the development of complex genetic models that can be used to simulate data is not always intuitive. In fact, only a few such models have been published. We have previously developed a genetic algorithm approach to discovering complex genetic models in which two single nucleotide polymorphisms (SNPs) influence disease risk solely through nonlinear interactions. In this paper, we extend this approach for the discovery of high-order epistasis models involving three to five SNPs. We demonstrate that the genetic algorithm is capable of routinely discovering interesting high-order epistasis models in which each SNP influences risk of disease only through interactions with the other SNPs in the model. This study opens the door for routine simulation of complex gene-gene interactions among SNPs for the development and evaluation of new statistical and computational approaches for identifying common, complex multifactorial disease susceptibility genes.

  11. TIP: protein backtranslation aided by genetic algorithms.

    Science.gov (United States)

    Moreira, Andrés; Maass, Alejandro

    2004-09-01

    Several applications require the backtranslation of a protein sequence into a nucleic acid sequence. The degeneracy of the genetic code makes this process ambiguous; moreover, not every translation is equally viable. The usual answer is to mimic the codon usage of the target species; however, this does not capture all the relevant features of the 'genomic styles' from different taxa. The program TIP ' Traducción Inversa de Proteínas') applies genetic algorithms to improve the backtranslation, by minimizing the difference of some coding statistics with respect to their average value in the target. http://www.cmm.uchile.cl/genoma/tip/

  12. 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.

  13. Fashion sketch design by interactive genetic algorithms

    Science.gov (United States)

    Mok, P. Y.; Wang, X. X.; Xu, J.; Kwok, Y. L.

    2012-11-01

    Computer aided design is vitally important for the modern industry, particularly for the creative industry. Fashion industry faced intensive challenges to shorten the product development process. In this paper, a methodology is proposed for sketch design based on interactive genetic algorithms. The sketch design system consists of a sketch design model, a database and a multi-stage sketch design engine. First, a sketch design model is developed based on the knowledge of fashion design to describe fashion product characteristics by using parameters. Second, a database is built based on the proposed sketch design model to define general style elements. Third, a multi-stage sketch design engine is used to construct the design. Moreover, an interactive genetic algorithm (IGA) is used to accelerate the sketch design process. The experimental results have demonstrated that the proposed method is effective in helping laypersons achieve satisfied fashion design sketches.

  14. Genetic algorithms and aquifer parameter identification

    Institute of Scientific and Technical Information of China (English)

    LI Jing-sheng(李竞生); YAO Lei-hua(姚磊华); LI Yang(李杨)

    2003-01-01

    In order to identify aquifer parameter,authors develops an improved combinatorial method called best chromosome clone plus younger generation chromosome prepotency genetic algorithm (BCC-YGCP-GA), based on a decimal system simple genetic algorithm (SGA). The paper takes unsteady state flows in a two-dimensional, inhomogeneous, confined aquifer for a ideal model, and utilizes SGA and BCC-YGCP-GA coupled to finite element method for identifying aquifer hydraulic conductivity K1,K2,K3 and storage S1,S2,S3, respectively. It is shown from the result that GSA does not reach convergence with 100 generations, whereas convergence rate of BCC-YGCD-GA is very fast. Objective function value calculated by BCC-YGCD-GA is 0.001 29 with 100 generations, and hydraulic conductivity and storage of three zones are almost equal to the "true" values of ideal model.

  15. Genetic algorithms for minimal source reconstructions

    Energy Technology Data Exchange (ETDEWEB)

    Lewis, P.S.; Mosher, J.C.

    1993-12-01

    Under-determined linear inverse problems arise in applications in which signals must be estimated from insufficient data. In these problems the number of potentially active sources is greater than the number of observations. In many situations, it is desirable to find a minimal source solution. This can be accomplished by minimizing a cost function that accounts from both the compatibility of the solution with the observations and for its ``sparseness``. Minimizing functions of this form can be a difficult optimization problem. Genetic algorithms are a relatively new and robust approach to the solution of difficult optimization problems, providing a global framework that is not dependent on local continuity or on explicit starting values. In this paper, the authors describe the use of genetic algorithms to find minimal source solutions, using as an example a simulation inspired by the reconstruction of neural currents in the human brain from magnetoencephalographic (MEG) measurements.

  16. 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

  17. Identification of Hammerstein Model Based on Quantum Genetic Algorithm

    OpenAIRE

    Zhang Hai Li

    2013-01-01

    Nonlinear system identification is a main topic of modern identification. A new method for nonlinear system identification is presented by using Quantum Genetic Algorithm(QGA).The problems of nonlinear system identification are cast as function optimization overprameter space,and the Quantum Genetic Algorithm is adopted to solve the optimization problem. Simulation experiments show that: compared with the genetic algorithm, quantum genetic algorithm is an effective swarm intelligence algorith...

  18. Design of hyperbolic metamaterials by genetic algorithm

    Science.gov (United States)

    Goforth, Ian A.; Alisafaee, Hossein; Fullager, Daniel B.; Rosenbury, Chris; Fiddy, Michael A.

    2014-09-01

    We explain the design of one dimensional Hyperbolic Metamaterials (HMM) using a genetic algorithm (GA) and provide sample applications including the realization of negative refraction. The design method is a powerful optimization approach to find the optimal performance of such structures, which "naturally" finds HMM structures that are globally optimized for specific applications. We explain how a fitness function can be incorporated into the GA for different metamaterial properties.

  19. Designing quantum gates using the genetic algorithm

    Science.gov (United States)

    Kumar, Karthikeyan S.; Paraoanu, G. S.

    2012-12-01

    We demonstrate the usage of Genetic Algorithm (GA) to tailor the radio frequency pulses for producing unitary transformations in qubit systems. We find that the initial population converges to the optimal solution after 10 generations, for a one segment pulse corresponding to single qubit Hadamard gate. For a two qubit CNOT gate, we see the population convergence for a two segment pulse after 150 generations. This demonstrates that the method is suitable for designing quantum gates.

  20. Genetic algorithms in computer aided inductor design

    OpenAIRE

    Jean Fivaz; Willem A. Cronjé

    2004-01-01

    The goal of this investigation is to determine the advantages of using genetic algorithms in computer-aided design as applied to inductors.  These advantages are exploited in design problems with a number of specifications and constraints, as encountered in power electronics during practical inductor design. The design tool should be able to select components, such as cores and wires, from databases of available components, and evaluate these choices based on the components’ characteristic d...

  1. Assembly line balancing using genetic algorithms

    OpenAIRE

    Tanyer, Muzaffer

    1997-01-01

    Ankara : Department of Industrial Engineering and Institute of Engineering and Sciences, Bilkent Univ., 1997. Thesis (Master's) -- Bilkent University, 1997. Includes bibliographical references leaves 69-73 For the last few decades, the genetic algorithms (GAs) have been used as a kind of heuristic in many areas of manufacturing. Facility layout, scheduling, process planning, and assembly line balancing are some of the areas where GAs are already popular. GAs are more efficie...

  2. Quantum-Inspired Genetic Algorithm or Quantum Genetic Algorithm: Which Is It?

    Science.gov (United States)

    Jones, Erika

    2015-04-01

    Our everyday work focuses on genetic algorithms (GAs) related to quantum computing where we call ``related'' algorithms those falling into one of two classes: (1) GAs run on classical computers but making use of quantum mechanical (QM) constructs and (2) GAs run on quantum hardware. Though convention has yet to be set with respect to usage of the accepted terms quantum-inspired genetic algorithm (QIGA) and quantum genetic algorithm (QGA), we find the two terms highly suitable respectively as labels for the aforementioned classes. With these specific definitions in mind, the difference between the QIGA and QGA is greater than might first be appreciated, particularly by those coming from a perspective emphasizing GA use as a general computational tool irrespective of QM aspects (1) suggested by QIGAs and (2) inherent in QGAs. We offer a theoretical standpoint highlighting key differences-both obvious, and more significantly, subtle-to be considered in general design of a QIGA versus that of a QGA.

  3. Genetic Algorithms for Multiple-Choice Problems

    CERN Document Server

    Aickelin, Uwe

    2010-01-01

    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 structur...

  4. Asian Option Pricing Based on Genetic Algorithms

    Institute of Scientific and Technical Information of China (English)

    YunzhongLiu; HuiyuXuan

    2004-01-01

    The cross-fertilization between artificial intelligence and computational finance has resulted in some of the most active research areas in financial engineering. One direction is the application of machine learning techniques to pricing financial products, which is certainly one of the most complex issues in finance. In the literature, when the interest rate,the mean rate of return and the volatility of the underlying asset follow general stochastic processes, the exact solution is usually not available. In this paper, we shall illustrate how genetic algorithms (GAs), as a numerical approach, can be potentially helpful in dealing with pricing. In particular, we test the performance of basic genetic algorithms by using it to the determination of prices of Asian options, whose exact solutions is known from Black-Scholesoption pricing theory. The solutions found by basic genetic algorithms are compared with the exact solution, and the performance of GAs is ewluated accordingly. Based on these ewluations, some limitations of GAs in option pricing are examined and possible extensions to future works are also proposed.

  5. Emergence of Algorithmic Languages in Genetic Systems

    CERN Document Server

    Angeles, O; Waelbroeck, H

    1997-01-01

    In genetic systems there is a non-trivial interface between the sequence of symbols which constitutes the chromosome, or ``genotype'', and the products which this sequence encodes --- the ``phenotype''. This interface can be thought of as a ``computer''. In this case the chromosome is viewed as an algorithm and the phenotype as the result of the computation. In general only a small fraction of all possible sequences of symbols makes any sense for a given computer. The difficulty of finding meaningful algorithms by random mutation is known as the brittleness problem. In this paper we show that mutation and crossover favour the emergence of an algorithmic language which facilitates the production of meaningful sequences following random mutations of the genotype. We base our conclusions on an analysis of the population dynamics of a variant of Kitano's neurogenetic model wherein the chromosome encodes the rules for cellular division and the phenotype is a 16-cell organism interpreted as a connectivity matrix fo...

  6. Optimization of unit commitment based on genetic algorithms

    Institute of Scientific and Technical Information of China (English)

    蔡兴国; 初壮

    2002-01-01

    How to solve unit commitment and load dispatch of power system by genetic algorithms is discussed in this paper. A combination encoding scheme of binary encoding and floating number encoding and corresponding genetic operators are developed. Meanwhile a contract mapping genetic algorithm is used to enhance traditional GA' s convergence. The result of a practical example shows that this algorithm is effective.

  7. External validation of a claims-based algorithm for classifying kidney-cancer surgeries

    Directory of Open Access Journals (Sweden)

    Deapen Dennis

    2009-06-01

    Full Text Available Abstract Background Unlike other malignancies, there is no literature supporting the accuracy of medical claims data for identifying surgical treatments among patients with kidney cancer. We sought to validate externally a previously published Medicare-claims-based algorithm for classifying surgical treatments among patients with early-stage kidney cancer. To achieve this aim, we compared procedure assignments based on Medicare claims with the type of surgery specified in SEER registry data and clinical operative reports. Methods Using linked SEER-Medicare data, we calculated the agreement between Medicare claims and SEER data for identification of cancer-directed surgery among 6,515 patients diagnosed with early-stage kidney cancer. Next, for a subset of 120 cases, we determined the agreement between the claims algorithm and the medical record. Finally, using the medical record as the reference-standard, we calculated the sensitivity, specificity, and positive and negative predictive values of the claims algorithm. Results Among 6,515 cases, Medicare claims and SEER data identified 5,483 (84.1% and 5,774 (88.6% patients, respectively, who underwent cancer-directed surgery (observed agreement = 93%, κ = 0.69, 95% CI 0.66 – 0.71. The two data sources demonstrated 97% agreement for classification of partial versus radical nephrectomy (κ = 0.83, 95% CI 0.81 – 0.86. We observed 97% agreement between the claims algorithm and clinical operative reports; the positive predictive value of the claims algorithm exceeded 90% for identification of both partial nephrectomy and laparoscopic surgery. Conclusion Medicare claims represent an accurate data source for ascertainment of population-based patterns of surgical care among patients with early-stage kidney cancer.

  8. Adaboost Ensemble with Simple Genetic Algorithm for Student Prediction Mode

    Directory of Open Access Journals (Sweden)

    AhmedSharaf ElDen

    2013-05-01

    Full Text Available Predicting the student performance is a great concern to the higher education managements.Thisprediction helps to identify and to improve students' performance.Several factors may improve thisperformance.In the present study, we employ the data mining processes, particularly classification, toenhance the quality of the higher educational system. Recently, a new direction is used for the improvementof the classification accuracy by combining classifiers.In thispaper, we design and evaluate a fastlearningalgorithm using AdaBoost ensemble with a simple genetic algorithmcalled “Ada-GA” where the geneticalgorithm is demonstrated to successfully improve the accuracy of the combined classifier performance.The Ada-GA algorithm proved to be of considerable usefulness in identifying the students at risk early,especially in very large classes. This early prediction allows the instructor to provide appropriate advisingto those students. The Ada/GA algorithm is implemented and tested on ASSISTments dataset, the resultsshowed that this algorithm hassuccessfully improved the detection accuracy as well as it reduces thecomplexity of computation.

  9. Explicit filtering of building blocks for genetic algorithms

    NARCIS (Netherlands)

    Kemenade, C.H.M. van

    1996-01-01

    Genetic 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 algorithms re

  10. A new training algorithm using artificial neural networks to classify gender-specific dynamic gait patterns.

    Science.gov (United States)

    Andrade, Andre; Costa, Marcelo; Paolucci, Leopoldo; Braga, Antônio; Pires, Flavio; Ugrinowitsch, Herbert; Menzel, Hans-Joachim

    2015-01-01

    The aim of this study was to present a new training algorithm using artificial neural networks called multi-objective least absolute shrinkage and selection operator (MOBJ-LASSO) applied to the classification of dynamic gait patterns. The movement pattern is identified by 20 characteristics from the three components of the ground reaction force which are used as input information for the neural networks in gender-specific gait classification. The classification performance between MOBJ-LASSO (97.4%) and multi-objective algorithm (MOBJ) (97.1%) is similar, but the MOBJ-LASSO algorithm achieved more improved results than the MOBJ because it is able to eliminate the inputs and automatically select the parameters of the neural network. Thus, it is an effective tool for data mining using neural networks. From 20 inputs used for training, MOBJ-LASSO selected the first and second peaks of the vertical force and the force peak in the antero-posterior direction as the variables that classify the gait patterns of the different genders.

  11. Effective Rule Based Classifier using Multivariate Filter and Genetic Miner for Mammographic Image Classification

    Directory of Open Access Journals (Sweden)

    Nirase Fathima Abubacker

    2015-06-01

    Full Text Available Mammography is an important examination in the early detection of breast abnormalities. Automatic classifications of mammogram images into normal, benign or malignant would help the radiologists in diagnosis of breast cancer cases. This study investigates the effectiveness of using rule-based classifiers with multivariate filter and genetic miner to classify mammogram images. The method discovers association rules with the classes as the consequence and classifies the images based on the Highest Average Confidence of the association rules (HAvC matched for the classes. In the association rules mining stage, Correlation based Feature Selection (CFS plays an enormous significance to reduce the complexity of image mining process is used in this study as a feature selection method and a modified genetic association rule mining technique, the GARM, is used to discover the rules. The method is evaluated on mammogram image dataset with 240 images taken from DDSM. The performance of the method is compared against other classifiers such as SMO; Naïve Bayes and J48. The performance of the proposed method is promising with 88% accuracy and outperforms other classifiers in the context of mammogram image classification.

  12. NOVEL QUANTUM-INSPIRED GENETIC ALGORITHM BASED ON IMMUNITY

    Institute of Scientific and Technical Information of China (English)

    Li Ying; Zhao Rongchun; Zhang Yanning; Jiao Licheng

    2005-01-01

    A novel algorithm, the Immune Quantum-inspired Genetic Algorithm (IQGA), is proposed by introducing immune concepts and methods into Quantum-inspired Genetic Algorithm (QGA). With the condition of preserving QGA's advantages, IQGA utilizes the characteristics and knowledge in the pending problems for restraining the repeated and ineffective operations during evolution, so as to improve the algorithm efficiency. The experimental results of the knapsack problem show that the performance of IQGA is superior to the Conventional Genetic Algorithm (CGA), the Immune Genetic Algorithm (IGA) and QGA.

  13. Identification of Hammerstein Model Based on Quantum Genetic Algorithm

    Directory of Open Access Journals (Sweden)

    Zhang Hai Li

    2013-07-01

    Full Text Available Nonlinear system identification is a main topic of modern identification. A new method for nonlinear system identification is presented by using Quantum Genetic Algorithm(QGA.The problems of nonlinear system identification are cast as function optimization overprameter space,and the Quantum Genetic Algorithm is adopted to solve the optimization problem. Simulation experiments show that: compared with the genetic algorithm, quantum genetic algorithm is an effective swarm intelligence algorithm, its salient features of the algorithm parameters, small population size, and the use of Quantum gate update populations, greatly improving the recognition in the optimization of speed and accuracy. Simulation results show the effectiveness of the proposed method.

  14. New Hybrid Genetic Algorithm for Vertex Cover Problems

    Institute of Scientific and Technical Information of China (English)

    霍红卫; 许进

    2003-01-01

    This paper presents a new hybrid genetic algorithm for the vertex cover problems in which scan-repair and local improvement techniques are used for local optimization. With the hybrid approach, genetic algorithms are used to perform global exploration in a population, while neighborhood search methods are used to perform local exploitation around the chromosomes. The experimental results indicate that hybrid genetic algorithms can obtain solutions of excellent quality to the problem instances with different sizes. The pure genetic algorithms are outperformed by the neighborhood search heuristics procedures combined with genetic algorithms.

  15. Improved Quantum Genetic Algorithm in Application of Scheduling Engineering Personnel

    Directory of Open Access Journals (Sweden)

    Huaixiao Wang

    2014-01-01

    Full Text Available To verify the availability of the improved quantum genetic algorithm in solving the scheduling engineering personnel problem, the following work has been carried out: the characteristics of the scheduling engineering personnel problem are analyzed, the quantum encoding method is proposed, and an improved quantum genetic algorithm is applied to address the issue. Taking the low efficiency and the bad performance of the conventional quantum genetic algorithm into account, a universal improved quantum genetic algorithm is introduced to solve the scheduling engineering personnel problem. Finally, the examples are applied to verify the effectiveness and superiority of the improved quantum genetic algorithm and the rationality of the encoding method.

  16. 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.

  17. 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.

  18. Using Genetic Algorithms in Secured Business Intelligence Mobile Applications

    OpenAIRE

    Silvia TRIF

    2011-01-01

    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 bette...

  19. Using Fuzzy Gaussian Inference and Genetic Programming to Classify 3D Human Motions

    Science.gov (United States)

    Khoury, Mehdi; Liu, Honghai

    This research introduces and builds on the concept of Fuzzy Gaussian Inference (FGI) (Khoury and Liu in Proceedings of UKCI, 2008 and IEEE Workshop on Robotic Intelligence in Informationally Structured Space (RiiSS 2009), 2009) as a novel way to build Fuzzy Membership Functions that map to hidden Probability Distributions underlying human motions. This method is now combined with a Genetic Programming Fuzzy rule-based system in order to classify boxing moves from natural human Motion Capture data. In this experiment, FGI alone is able to recognise seven different boxing stances simultaneously with an accuracy superior to a GMM-based classifier. Results seem to indicate that adding an evolutionary Fuzzy Inference Engine on top of FGI improves the accuracy of the classifier in a consistent way.

  20. Novel Quantum Genetic Algorithm and Its Applications

    Institute of Scientific and Technical Information of China (English)

    ZHANG Ge-xiang; LI Na; JIN Wei-dong; HU Lai-zhao

    2006-01-01

    By introducing strong parallelism of quantum computing into evolutionary algorithm,a novel quantum genetic algorithm (NQGA) is proposed.In NQGA,a novel approach for updating the rotation angles of quantum logic gates and a strategy for enhancing search capability and avoiding premature convergence are adopted.Several typical complex continuous functions are chosen to test the performance of NQGA.Also,NQGA is applied in selecting the best feature subset from a large number of features in radar emitter signal recognition.The testing and experimental results of feature selection show that NQGA presents good search capability,rapid convergence,short computing time,and ability to avoid premature convergence effectively.

  1. 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...

  2. Implementation of Genetic Algorithm in Predicting Diabetes

    Directory of Open Access Journals (Sweden)

    S.Sapna

    2012-01-01

    Full Text Available Data Mining aims at discovering knowledge out of data and presenting it in a form that is easily compressible to humans. Data Mining represents a process developed to examine large amounts of data routinely collected. The term also refers to a collection of tools used to perform the process. One of the useful applications in the field of medicine is the incurable chronic disease diabetes. Data Mining algorithm is used for testing the accuracy in predicting diabetic status. Fuzzy Systems are been used for solving a wide range of problems in different application domain Genetic Algorithm for designing. Fuzzy systems allows in introducing the learning and adaptation capabilities. Neural Networks are efficiently used for learning membership functions. Diabetes occurs throughout the world, but Type 2 is more common in the most developed countries. The greater increase in prevalence is however expected in Asia and Africa where most patients will likely be found by 2030.

  3. CERTAIN INVESTIGATIONS ON VARIOUS ALGORITHMS THAT IS USED TO CLASSIFY MALWARE AND GOODWARE IN ANDROID APPLICATIONS

    Directory of Open Access Journals (Sweden)

    B.P. Sreejith Vignesh

    2016-10-01

    Full Text Available In recent trends, the mobile devices play a very vital role in day to day activities of human beings. Google Android OS appeared lately i.e., in September 2008 in mobile market and gains more popularity. Google Android OS offers more flexibility for the users by offering N number of free downloadable applications to the users, which in turn gets changed as the superlative target for the attackers . As a result, many android applications that may contain the malware applications which are capable of stealing privacy information of users are available in market as a (.apk file. The attackers started to target uneducated people and started stealing the information using applications. These applications request user to allow set of permissions during installation. For a new user it is difficult to identify the set of permissions that are harmful. This could be an advantage for malware intruders to access the data or infect the mobile device by introducing malware applications. Therefore, android malware detection various algorithms algorithm and Machine learning approaches is proposed to classify malware and goodware applications by analyzing the permission features.

  4. Evolutionary algorithms in genetic regulatory networks model

    CERN Document Server

    Raza, Khalid

    2012-01-01

    Genetic Regulatory Networks (GRNs) plays a vital role in the understanding of complex biological processes. Modeling GRNs is significantly important in order to reveal fundamental cellular processes, examine gene functions and understanding their complex relationships. Understanding the interactions between genes gives rise to develop better method for drug discovery and diagnosis of the disease since many diseases are characterized by abnormal behaviour of the genes. In this paper we have reviewed various evolutionary algorithms-based approach for modeling GRNs and discussed various opportunities and challenges.

  5. The genetic algorithm for a signal enhancement

    Energy Technology Data Exchange (ETDEWEB)

    Karimova, L. [Laboratory of Computer Modelling, Institute of Mathematics, Pushkin Street 125, 480100 Almaty (Kazakhstan)]. E-mail: karimova@math.kz; Kuadykov, E. [Laboratory of Computer Modelling, Institute of Mathematics, Pushkin Street 125, 480100 Almaty (Kazakhstan); Makarenko, N. [Laboratory of Computer Modelling, Institute of Mathematics, Pushkin Street 125, 480100 Almaty (Kazakhstan)

    2004-11-21

    The paper is devoted to the problem of time series enhancement, which is based on the analysis of local regularity. The model construction using this analysis does not require any a priori assumption on the structure of the noise and the functional relationship between original signal and noise. The signal itself may be nowhere differentiable with rapidly varying local regularity, what is overcome with the help of the new technique of increasing the local Hoelder regularity of the signal under research. A new signal with prescribed regularity is constructed using the genetic algorithm. This approach is applied to enhancement of time series in the paleoclimatology, solar physics, dendrochronology, meteorology and hydrology.

  6. Genetic Algorithm for Hierarchical Wireless Sensor Networks

    Directory of Open Access Journals (Sweden)

    Sajid Hussain

    2007-09-01

    Full Text Available Large scale wireless sensor networks (WSNs can be used for various pervasive and ubiquitous applications such as security, health-care, industry automation, agriculture, environment and habitat monitoring. As hierarchical clusters can reduce the energy consumption requirements for WSNs, we investigate intelligent techniques for cluster formation and management. A genetic algorithm (GA is used to create energy efficient clusters for data dissemination in wireless sensor networks. The simulation results show that the proposed intelligent hierarchical clustering technique can extend the network lifetime for different network deployment environments.

  7. Genetic algorithms in computer aided inductor design

    Directory of Open Access Journals (Sweden)

    Jean Fivaz

    2004-09-01

    Full Text Available The goal of this investigation is to determine the advantages of using genetic algorithms in computer-aided design as applied to inductors.  These advantages are exploited in design problems with a number of specifications and constraints, as encountered in power electronics during practical inductor design. The design tool should be able to select components, such as cores and wires, from databases of available components, and evaluate these choices based on the components’ characteristic data read from a database of manufacturers’ data-sheets.  The proposed design must always be practically realizable, as close to the desired specifications as possible and within any specified constraints.

  8. Parallel Genetic Algorithm for Alpha Spectra Fitting

    Science.gov (United States)

    García-Orellana, Carlos J.; Rubio-Montero, Pilar; González-Velasco, Horacio

    2005-01-01

    We present a performance study of alpha-particle spectra fitting using parallel Genetic Algorithm (GA). The method uses a two-step approach. In the first step we run parallel GA to find an initial solution for the second step, in which we use Levenberg-Marquardt (LM) method for a precise final fit. GA is a high resources-demanding method, so we use a Beowulf cluster for parallel simulation. The relationship between simulation time (and parallel efficiency) and processors number is studied using several alpha spectra, with the aim of obtaining a method to estimate the optimal processors number that must be used in a simulation.

  9. 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.

  10. OIL SPILL DETECTION IN SAR IMAGES USING TEXTURE ENTROPY ALGORITHM AND MAHALANOBIS CLASSIFIER

    Directory of Open Access Journals (Sweden)

    POONAM M BHOGLE

    2012-12-01

    Full Text Available Oil spill has become critical in some countries, especially for countries that have seas or oceans. The situation has caused damage to the environment and polluted the water. To reduce environment damage and protect life in water, plants and soil near to disaster area .Study and analysis should be carried out .The causes and factorsthat lead to the disaster of oil spill should be studied or investigated. To analyze the problem of oil spill we consider 2 algorithms. These methods help in the analysis and identification of oil spill in SAR images. Since the 1980s, satellite-borne synthetic aperture radar (SAR has been investigated for early warning andmonitoring of marine oil spills to permit effective satellite surveillance in the marine environment. Synthetic Aperture Radar (SAR imaging system is used to monitor the marine system. Oil spill pollution plays a significant role in damaging marine ecosystem. One main advantages of SAR is that it can generate imagery under all weather conditions. Automated detection of oil spills from satellite SAR intensity imagery consists of three steps: Detection of dark spots , Extraction of features from the detected dark spots and classification of the dark spots into oil spills and look-alikes.Texture Entropy Algorithm is a method based on the utilization of texture algorithms for the discrimination of oil spill areas from the surrounding features, e.g. sea surface and look-alikes. Mahalanobis Classifier method first estimates covariance matrix and then Mahalanobis Distance is calculated for identification of oil spill or lookalike.

  11. Classification of Noisy Data: An Approach Based on Genetic Algorithms and Voronoi Tessellation

    DEFF Research Database (Denmark)

    Khan, Abdul Rauf; Schiøler, Henrik; Knudsen, Torben

    2016-01-01

    on the portioning of information space; and (2) use of the genetic algorithm to solve combinatorial problems for classification. In particular, we will implement our methodology to solve complex classification problems and compare the performance of our classifier with other well-known methods (SVM, KNN, and ANN...

  12. Classification of Noisy Data: An Approach Based on Genetic Algorithms and Voronoi Tessellation

    DEFF Research Database (Denmark)

    Khan, Abdul Rauf; Schiøler, Henrik; Knudsen, Torben

    on the portioning of information space; and (2) use of the genetic algorithm to solve combinatorial problems for classification. In particular, we will implement our methodology to solve complex classification problems and compare the performance of our classifier with other well-known methods (SVM, KNN, and ANN...

  13. The Integration of Cooperation Model and Genetic Algorithm

    Institute of Scientific and Technical Information of China (English)

    2002-01-01

    In the photogrammetry,some researchers have applied genetic algorithms in aerial image texture classification and reducing hyper-spectrum remote sensing data.Genetic algorithm can rapidly find the solutions which are close to the optimal solution.But it is not easy to find the optimal solution.In order to solve the problem,a cooperative evolution idea integrating genetic algorithm and ant colony algorithm is presented in this paper.On the basis of the advantages of ant colony algorithm,this paper proposes the method integrating genetic algorithms and ant colony algorithm to overcome the drawback of genetic algorithms.Moreover,the paper takes designing texture classification masks of aerial images as an example to illustrate the integration theory and procedures.

  14. Optimal Genetic View Selection Algorithm for Data Warehouse

    Institute of Scientific and Technical Information of China (English)

    Wang Ziqiang; Feng Boqin

    2005-01-01

    To efficiently solve the materialized view selection problem, an optimal genetic algorithm of how to select a set of views to be materialized is proposed so as to achieve both good query performance and low view maintenance cost under a storage space constraint. First, a pre-processing algorithm based on the maximum benefit per unit space is used to generate initial solutions. Then, the initial solutions are improved by the genetic algorithm having the mixture of optimal strategies. Furthermore, the generated infeasible solutions during the evolution process are repaired by loss function. The experimental results show that the proposed algorithm outperforms the heuristic algorithm and canonical genetic algorithm in finding optimal solutions.

  15. Modeling of genetic algorithms with a finite population

    NARCIS (Netherlands)

    Kemenade, C.H.M. van

    1997-01-01

    Cross-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 algorithms, all in

  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. Cognitive Radio — Genetic Algorithm Approach

    Science.gov (United States)

    Reddy, Y. B.

    2005-03-01

    Cognitive Radio (CR) is relatively a new technology, which intelligently detects a particular segment of the radio spectrum currently in use and selects unused spectrum quickly without interfering the transmission of authorized users. Cognitive Radios can learn about current use of spectrum in their operating area, make intelligent decisions, and react to immediate changes in the use of spectrum by other authorized users. The goal of CR technology is to relieve radio spectrum overcrowding, which actually translates to a lack of access to full radio spectrum utilization. Due to this adaptive behavior, the CR can easily avoid the interference of signals in a crowded radio frequency spectrum. In this research, we discuss the possible application of genetic algorithms (GA) to create a CR that can respond intelligently in changing and unanticipated circumstances and in the presence of hostile jammers and interferers. Genetic algorithms are problem solving techniques based on evolution and natural selection. GA models adapt Charles Darwin's evolutionary theory for analysis of data and interchanging design elements in hundreds of thousands of different combinations. Only the best-performing combinations are permitted to survive, and those combinations "reproduce" further, progressively yielding better and better results.

  18. Edge Crossing Minimization Algorithm for Hierarchical Graphs Based on Genetic Algorithms

    Institute of Scientific and Technical Information of China (English)

    2001-01-01

    We present an edge crossing minimization algorithm forhierarchical gr aphs based on genetic algorithms, and comparing it with some heuristic algorithm s. The proposed algorithm is more efficient and has the following advantages: th e frame of the algorithms is unified, the method is simple, and its implementati on and revision are easy.

  19. 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).

  20. Evolving Neural Network Using Variable String Genetic Algorithm for Color Infrared Aerial Image Classification

    Institute of Scientific and Technical Information of China (English)

    FU Xiaoyang; P E R Dale; ZHANG Shuqing

    2008-01-01

    Coastal wetlands are characterized by complex patterns both in their geomorphic and ecological features.Besides field observations,it is necessary to analyze the land cover of wetlands through the color infrared (CIR) aerial photography or remote sensing image.In this paper,we designed an evolving neural network classifier using variable string genetic algorithm (VGA) for the land cover classification of CIR aerial image.With the VGA,the classifier that we designed is able to evolve automatically the appropriate number of hidden nodes for modeling the neural network topology optimally and to find a near-optimal set of connection weights globally.Then,with backpropagation algorithm (BP),it can find the best connection weights.The VGA-BP classifier,which is derived from hybrid algorithms mentioned above,is demonstrated on CIR images classification effectively.Compared with standard classifiers,such as Bayes maximum-likelihood classifier,VGA classifier and BP-MLP (multi-layer perception) classifier,it has shown that the VGA-BP classifier can have better performance on highly resolution land cover classification.

  1. Chaotic genetic algorithm for gene selection and classification problems.

    Science.gov (United States)

    Chuang, Li-Yeh; Yang, Cheng-San; Li, Jung-Chike; Yang, Cheng-Hong

    2009-10-01

    Pattern recognition techniques suffer from a well-known curse, the dimensionality problem. The microarray data classification problem is a classical complex pattern recognition problem. Selecting relevant genes from microarray data poses a formidable challenge to researchers due to the high-dimensionality of features, multiclass categories being involved, and the usually small sample size. The goal of feature (gene) selection is to select those subsets of differentially expressed genes that are potentially relevant for distinguishing the sample classes. In this paper, information gain and chaotic genetic algorithm are proposed for the selection of relevant genes, and a K-nearest neighbor with the leave-one-out crossvalidation method serves as a classifier. The chaotic genetic algorithm is modified by using the chaotic mutation operator to increase the population diversity. The enhanced population diversity expands the GA's search ability. The proposed approach is tested on 10 microarray data sets from the literature. The experimental results show that the proposed method not only effectively reduced the number of gene expression levels, but also achieved lower classification error rates than other methods.

  2. Order Reduction of Linear Interval Systems Using Genetic Algorithm

    Directory of Open Access Journals (Sweden)

    Dr. Rajendra Prasad

    2010-10-01

    Full Text Available This paper presents an algorithm for order reduction of higher order linear interval system into stable lower order linear interval system by means of Genetic algorithm. In this algorithm the numerator and denominator polynomials are determined by minimizing the Integral square error (ISE using genetic algorithm (GA. The algorithm is simple, rugged and computer oriented. It is shown that the algorithm has several advantages, e.g. the reduced order models retain the steady-state value and stability of the original system. A numerical example illustrates the proposed algorithm.

  3. Novel scoring system and algorithm for classifying chronic rhinosinusitis: the JESREC Study.

    Science.gov (United States)

    Tokunaga, T; Sakashita, M; Haruna, T; Asaka, D; Takeno, S; Ikeda, H; Nakayama, T; Seki, N; Ito, S; Murata, J; Sakuma, Y; Yoshida, N; Terada, T; Morikura, I; Sakaida, H; Kondo, K; Teraguchi, K; Okano, M; Otori, N; Yoshikawa, M; Hirakawa, K; Haruna, S; Himi, T; Ikeda, K; Ishitoya, J; Iino, Y; Kawata, R; Kawauchi, H; Kobayashi, M; Yamasoba, T; Miwa, T; Urashima, M; Tamari, M; Noguchi, E; Ninomiya, T; Imoto, Y; Morikawa, T; Tomita, K; Takabayashi, T; Fujieda, S

    2015-08-01

    Chronic rhinosinusitis (CRS) can be classified into CRS with nasal polyps (CRSwNP) and CRS without nasal polyps (CRSsNP). CRSwNP displays more intense eosinophilic infiltration and the presence of Th2 cytokines. Mucosal eosinophilia is associated with more severe symptoms and often requires multiple surgeries because of recurrence; however, even in eosinophilic CRS (ECRS), clinical course is variable. In this study, we wanted to set objective clinical criteria for the diagnosis of refractory CRS. This was a retrospective study conducted by 15 institutions participating in the Japanese Epidemiological Survey of Refractory Eosinophilic Chronic Rhinosinusitis (JESREC). We evaluated patients with CRS treated with endoscopic sinus surgery (ESS), and risk of recurrence was estimated using Cox proportional hazard models. Multiple logistic regression models and receiver operating characteristics curves were constructed to create the diagnostic criterion for ECRS. We analyzed 1716 patients treated with ESS. To diagnose ECRS, the JESREC scoring system assessed unilateral or bilateral disease, the presence of nasal polyps, blood eosinophilia, and dominant shadow of ethmoid sinuses in computed tomography (CT) scans. The cutoff value of the score was 11 points (sensitivity: 83%, specificity: 66%). Blood eosinophilia (>5%), ethmoid sinus disease detected by CT scan, bronchial asthma, aspirin, and nonsteroidal anti-inflammatory drugs intolerance were associated significantly with recurrence. We subdivided CRSwNP in non-ECRS, mild, moderate, and severe ECRS according to our algorithm. This classification was significantly correlated with prognosis. It is notable that this algorithm may give useful information to clinicians in the refractoriness of CRS before ESS or biopsy. © 2015 The Authors. Allergy Published by John Wiley & Sons Ltd.

  4. Design of fuzzy classifier for diabetes disease using Modified Artificial Bee Colony algorithm.

    Science.gov (United States)

    Beloufa, Fayssal; Chikh, M A

    2013-10-01

    In this study, diagnosis of diabetes disease, which is one of the most important diseases, is conducted with artificial intelligence techniques. We have proposed a novel Artificial Bee Colony (ABC) algorithm in which a mutation operator is added to an Artificial Bee Colony for improving its performance. When the current best solution cannot be updated, a blended crossover operator (BLX-α) of genetic algorithm is applied, in order to enhance the diversity of ABC, without compromising with the solution quality. This modified version of ABC is used as a new tool to create and optimize automatically the membership functions and rules base directly from data. We take the diabetes dataset used in our work from the UCI machine learning repository. The performances of the proposed method are evaluated through classification rate, sensitivity and specificity values using 10-fold cross-validation method. The obtained classification rate of our method is 84.21% and it is very promising when compared with the previous research in the literature for the same problem. Copyright © 2013 Elsevier Ireland Ltd. All rights reserved.

  5. 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.

  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. Intrusion detection: a novel approach that combines boosting genetic fuzzy classifier and data mining techniques

    Science.gov (United States)

    Ozyer, Tansel; Alhajj, Reda; Barker, Ken

    2005-03-01

    This paper proposes an intelligent intrusion detection system (IDS) which is an integrated approach that employs fuzziness and two of the well-known data mining techniques: namely classification and association rule mining. By using these two techniques, we adopted the idea of using an iterative rule learning that extracts out rules from the data set. Our final intention is to predict different behaviors in networked computers. To achieve this, we propose to use a fuzzy rule based genetic classifier. Our approach has two main stages. First, fuzzy association rule mining is applied and a large number of candidate rules are generated for each class. Then the rules pass through pre-screening mechanism in order to reduce the fuzzy rule search space. Candidate rules obtained after pre-screening are used in genetic fuzzy classifier to generate rules for the specified classes. Classes are defined as Normal, PRB-probe, DOS-denial of service, U2R-user to root and R2L- remote to local. Second, an iterative rule learning mechanism is employed for each class to find its fuzzy rules required to classify data each time a fuzzy rule is extracted and included in the system. A Boosting mechanism evaluates the weight of each data item in order to help the rule extraction mechanism focus more on data having relatively higher weight. Finally, extracted fuzzy rules having the corresponding weight values are aggregated on class basis to find the vote of each class label for each data item.

  8. 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.

  9. Improved Quantum Genetic Algorithm in Application of Scheduling Engineering Personnel

    OpenAIRE

    Huaixiao Wang; Ling Li; Jianyong Liu; Yong Wang; Chengqun Fu

    2014-01-01

    To verify the availability of the improved quantum genetic algorithm in solving the scheduling engineering personnel problem, the following work has been carried out: the characteristics of the scheduling engineering personnel problem are analyzed, the quantum encoding method is proposed, and an improved quantum genetic algorithm is applied to address the issue. Taking the low efficiency and the bad performance of the conventional quantum genetic algorithm into account, a universal improved q...

  10. Genetic algorithm and particle swarm optimization combined with Powell method

    Science.gov (United States)

    Bento, David; Pinho, Diana; Pereira, Ana I.; Lima, Rui

    2013-10-01

    In recent years, the population algorithms are becoming increasingly robust and easy to use, based on Darwin's Theory of Evolution, perform a search for the best solution around a population that will progress according to several generations. This paper present variants of hybrid genetic algorithm - Genetic Algorithm and a bio-inspired hybrid algorithm - Particle Swarm Optimization, both combined with the local method - Powell Method. The developed methods were tested with twelve test functions from unconstrained optimization context.

  11. Solving Hitchcock's transportation problem by a genetic algorithm

    Institute of Scientific and Technical Information of China (English)

    CHEN Hai-feng; CHO Joong Rae; LEE Jeong.Tae

    2004-01-01

    Genetic algorithms (GAs) employ the evolutionary process of Darwin's nature selection theory to find the solutions of optimization problems. In this paper, an implementation of genetic algorithm is put forward to solve a classical transportation problem, namely the Hitchcock's Transportation Problem (HTP), and the GA is improved to search for all optimal solutions and identify them automatically. The algorithm is coded with C++ and validated by numerical examples. The computational results show that the algorithm is efficient for solving the Hitchcock's transportation problem.

  12. 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....

  13. 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....

  14. 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.

  15. A Parallel Genetic Simulated Annealing Hybrid Algorithm for Task Scheduling

    Institute of Scientific and Technical Information of China (English)

    SHU Wanneng; ZHENG Shijue

    2006-01-01

    In this paper combined with the advantages of genetic algorithm and simulated annealing, brings forward a parallel genetic simulated annealing hybrid algorithm (PGSAHA) and applied to solve task scheduling problem in grid computing .It first generates a new group of individuals through genetic operation such as reproduction, crossover, mutation, etc, and than simulated anneals independently all the generated individuals respectively.When the temperature in the process of cooling no longer falls, the result is the optimal solution on the whole.From the analysis and experiment result, it is concluded that this algorithm is superior to genetic algorithm and simulated annealing.

  16. 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.

  17. A Comparison between Memetic algorithm and Genetic algorithm for the cryptanalysis of Simplified Data Encryption Standard algorithm

    CERN Document Server

    Garg, Poonam

    2010-01-01

    Genetic algorithms are a population-based Meta heuristics. They have been successfully applied to many optimization problems. However, premature convergence is an inherent characteristic of such classical genetic algorithms that makes them incapable of searching numerous solutions of the problem domain. A memetic algorithm is an extension of the traditional genetic algorithm. It uses a local search technique to reduce the likelihood of the premature convergence. The cryptanalysis of simplified data encryption standard can be formulated as NP-Hard combinatorial problem. In this paper, a comparison between memetic algorithm and genetic algorithm were made in order to investigate the performance for the cryptanalysis on simplified data encryption standard problems(SDES). The methods were tested and various experimental results show that memetic algorithm performs better than the genetic algorithms for such type of NP-Hard combinatorial problem. This paper represents our first effort toward efficient memetic algo...

  18. Integrating Genetic Algorithm, Tabu Search Approach for Job Shop Scheduling

    CERN Document Server

    Thamilselvan, R

    2009-01-01

    This paper presents a new algorithm based on integrating Genetic Algorithms and Tabu Search methods to solve the Job Shop Scheduling problem. The idea of the proposed algorithm is derived from Genetic Algorithms. Most of the scheduling problems require either exponential time or space to generate an optimal answer. Job Shop scheduling (JSS) is the general scheduling problem and it is a NP-complete problem, but it is difficult to find the optimal solution. This paper applies Genetic Algorithms and Tabu Search for Job Shop Scheduling problem and compares the results obtained by each. With the implementation of our approach the JSS problems reaches optimal solution and minimize the makespan.

  19. A genetic algorithm selection perturbative hyper-heuristic for solving ...

    African Journals Online (AJOL)

    http://dx.doi.org/10.5784/31-1-158. 39 ... in solving other combinatorial optimisation problems, this paper investigates the use of a genetic ...... [16] Goldberg D, 1989, Genetic Algorithms in Search, Optimization and Machine Learning, Addison-.

  20. 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.

  1. Anisotropic selection in cellular genetic algorithms

    CERN Document Server

    Simoncini, David; Collard, Philippe; Clergue, Manuel

    2008-01-01

    In this paper we introduce a new selection scheme in cellular genetic algorithms (cGAs). Anisotropic Selection (AS) promotes diversity and allows accurate control of the selective pressure. First we compare this new scheme with the classical rectangular grid shapes solution according to the selective pressure: we can obtain the same takeover time with the two techniques although the spreading of the best individual is different. We then give experimental results that show to what extent AS promotes the emergence of niches that support low coupling and high cohesion. Finally, using a cGA with anisotropic selection on a Quadratic Assignment Problem we show the existence of an anisotropic optimal value for which the best average performance is observed. Further work will focus on the selective pressure self-adjustment ability provided by this new selection scheme.

  2. ATM cash management using genetic algorithm

    Directory of Open Access Journals (Sweden)

    Ahmadreza Ghodrati

    2013-07-01

    Full Text Available Automatic teller machine (ATM is one of the most popular banking facilities to do daily financial transactions. People use ATM services to pay bills, transfer funds and withdraw cash. Therefore, we can treat ATM as a tradition inventory problem and use simulation technique to analysis the amount of cash required on different occasions such as regular days, holidays, etc. The proposed model of this paper uses genetic algorithm to determine the replenishment cash strategy for each ATM. The survey uses all transactions accomplished during the fiscal years of 2011-2012 on one of Iranian banks named Ayande. The study categorizes various ATM based on the average daily transactions into three groups of low, medium and high levels. The preliminary results of our survey indicate that it is possible to do setup different strategies to manage cash in various banks, optimally.

  3. Genetic Algorithm Approaches to Prebiobiotic Chemistry Modeling

    Science.gov (United States)

    Lohn, Jason; Colombano, Silvano

    1997-01-01

    We model an artificial chemistry comprised of interacting polymers by specifying two initial conditions: a distribution of polymers and a fixed set of reversible catalytic reactions. A genetic algorithm is used to find a set of reactions that exhibit a desired dynamical behavior. Such a technique is useful because it allows an investigator to determine whether a specific pattern of dynamics can be produced, and if it can, the reaction network found can be then analyzed. We present our results in the context of studying simplified chemical dynamics in theorized protocells - hypothesized precursors of the first living organisms. Our results show that given a small sample of plausible protocell reaction dynamics, catalytic reaction sets can be found. We present cases where this is not possible and also analyze the evolved reaction sets.

  4. 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.

  5. Optimized dynamical decoupling via genetic algorithms

    Science.gov (United States)

    Quiroz, Gregory; Lidar, Daniel A.

    2013-11-01

    We utilize genetic algorithms aided by simulated annealing to find optimal dynamical decoupling (DD) sequences for a single-qubit system subjected to a general decoherence model under a variety of control pulse conditions. We focus on the case of sequences with equal pulse intervals and perform the optimization with respect to pulse type and order. In this manner, we obtain robust DD sequences, first in the limit of ideal pulses, then when including pulse imperfections such as finite-pulse duration and qubit rotation (flip-angle) errors. Although our optimization is numerical, we identify a deterministic structure that underlies the top-performing sequences. We use this structure to devise DD sequences which outperform previously designed concatenated DD (CDD) and quadratic DD (QDD) sequences in the presence of pulse errors. We explain our findings using time-dependent perturbation theory and provide a detailed scaling analysis of the optimal sequences.

  6. Optimized Dynamical Decoupling via Genetic Algorithms

    CERN Document Server

    Quiroz, Gregory

    2013-01-01

    We utilize genetic algorithms to find optimal dynamical decoupling (DD) sequences for a single-qubit system subjected to a general decoherence model under a variety of control pulse conditions. We focus on the case of sequences with equal pulse-intervals and perform the optimization with respect to pulse type and order. In this manner we obtain robust DD sequences, first in the limit of ideal pulses, then when including pulse imperfections such as finite pulse duration and qubit rotation (flip-angle) errors. Although our optimization is numerical, we identify a deterministic structure underlies the top-performing sequences. We use this structure to devise DD sequences which outperform previously designed concatenated DD (CDD) and quadratic DD (QDD) sequences in the presence of pulse errors. We explain our findings using time-dependent perturbation theory and provide a detailed scaling analysis of the optimal sequences.

  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. DNA Technique, cryptography, bit exchange, Genetic Algorithm

    Directory of Open Access Journals (Sweden)

    Meenakshi Moza

    2016-07-01

    Full Text Available Internet reliability and performance is based mostly on the underlying routing protocols. The current traffic load has to be taken into account for computation of paths in routing protocols. Addressing the selection of path, from a known source to destination is the basic aim of this paper. Making use of multipoint crossover and mutation is done for optimum and when required alternate path determination. Network scenario which consists of nodes that are fixed and limited to the known size of topology, comprises the population size. This paper proposes a simple method of calculating the shortest path for a network using Genetic Algorithm (GA, which is capable of giving an efficient, dynamic and consistent solution in spite of, what topology, changes in link and node happen and volume of the network. GA is used in this paper for optimization of routing. It helps us in enhancing the performance of the routers.

  9. An environment-adaptive management algorithm for hearing-support devices incorporating listening situation and noise type classifiers.

    Science.gov (United States)

    Yook, Sunhyun; Nam, Kyoung Won; Kim, Heepyung; Hong, Sung Hwa; Jang, Dong Pyo; Kim, In Young

    2015-04-01

    In order to provide more consistent sound intelligibility for the hearing-impaired person, regardless of environment, it is necessary to adjust the setting of the hearing-support (HS) device to accommodate various environmental circumstances. In this study, a fully automatic HS device management algorithm that can adapt to various environmental situations is proposed; it is composed of a listening-situation classifier, a noise-type classifier, an adaptive noise-reduction algorithm, and a management algorithm that can selectively turn on/off one or more of the three basic algorithms-beamforming, noise-reduction, and feedback cancellation-and can also adjust internal gains and parameters of the wide-dynamic-range compression (WDRC) and noise-reduction (NR) algorithms in accordance with variations in environmental situations. Experimental results demonstrated that the implemented algorithms can classify both listening situation and ambient noise type situations with high accuracies (92.8-96.4% and 90.9-99.4%, respectively), and the gains and parameters of the WDRC and NR algorithms were successfully adjusted according to variations in environmental situation. The average values of signal-to-noise ratio (SNR), frequency-weighted segmental SNR, Perceptual Evaluation of Speech Quality, and mean opinion test scores of 10 normal-hearing volunteers of the adaptive multiband spectral subtraction (MBSS) algorithm were improved by 1.74 dB, 2.11 dB, 0.49, and 0.68, respectively, compared to the conventional fixed-parameter MBSS algorithm. These results indicate that the proposed environment-adaptive management algorithm can be applied to HS devices to improve sound intelligibility for hearing-impaired individuals in various acoustic environments.

  10. GAMPMS: Genetic algorithm managed peptide mutant screening.

    Science.gov (United States)

    Long, Thomas; McDougal, Owen M; Andersen, Tim

    2015-06-30

    The prominence of endogenous peptide ligands targeted to receptors makes peptides with the desired binding activity good molecular scaffolds for drug development. Minor modifications to a peptide's primary sequence can significantly alter its binding properties with a receptor, and screening collections of peptide mutants is a useful technique for probing the receptor-ligand binding domain. Unfortunately, the combinatorial growth of such collections can limit the number of mutations which can be explored using structure-based molecular docking techniques. Genetic algorithm managed peptide mutant screening (GAMPMS) uses a genetic algorithm to conduct a heuristic search of the peptide's mutation space for peptides with optimal binding activity, significantly reducing the computational requirements of the virtual screening. The GAMPMS procedure was implemented and used to explore the binding domain of the nicotinic acetylcholine receptor (nAChR) α3β2-isoform with a library of 64,000 α-conotoxin (α-CTx) MII peptide mutants. To assess GAMPMS's performance, it was compared with a virtual screening procedure that used AutoDock to predict the binding affinity of each of the α-CTx MII peptide mutants with the α3β2-nAChR. The GAMPMS implementation performed AutoDock simulations for as few as 1140 of the 64,000 α-CTx MII peptide mutants and could consistently identify a set of 10 peptides with an aggregated binding energy that was at least 98% of the aggregated binding energy of the 10 top peptides from the exhaustive AutoDock screening.

  11. EVALUATION OF SOUND CLASSIFICATION USING MODIFIED CLASSIFIER AND SPEECH ENHANCEMENT USING ICA ALGORITHM FOR HEARING AID APPLICATION

    Directory of Open Access Journals (Sweden)

    N. Shanmugapriya

    2016-03-01

    Full Text Available Hearing aid users are exposed to diversified vocal scenarios. The necessity for sound classification algorithms becomes a vital factor to yield good listening experience. In this work, an approach is proposed to improve the speech quality in the hearing aids based on Independent Component Analysis (ICA algorithm with modified speech signal classification methods. The proposed algorithm has better results on speech intelligibility than other existing algorithm and this result has been proved by the intelligibility experiments. The ICA algorithm and modified Bayesian with Adaptive Neural Fuzzy Interference System (ANFIS is to effectiveness of the strategies of speech quality, thus this classification increases noise resistance of the new speech processing algorithm that proposed in this present work. This proposed work indicates that the new Modified classifier can be feasible in hearing aid applications.

  12. Chemometrics: From classical to genetic algorithms

    Directory of Open Access Journals (Sweden)

    Leardi, Riccardo

    2002-03-01

    Full Text Available In this paper the fundamentals of Chemometrics are presented, by means of a quick overview of the most relevant techniques for data display, classification, modeling and calibration. Two emerging techniques such as Genetic Algorithms and Artificial Neural Networks will also be presented. Goal of the paper is to make people aware of the great superiority of multivariate analysis over the commonly used univariate approach. Mathematical and algorithmical details are not presented, since the paper is mainly focused on the general problems to which Chemometrics can be successfully applied in the field of Food Chemistry.En este artículo se muestran los aspectos fundamentales de la Quimiometria por medio de una revisión rápida de las técnicas más relevantes para mostrar los datos, modelar y calibrar. Se describen dos técnicas emergentes como los algoritmos genéticos y las redes neuronales. El objetivo del articulo es que la comunidad científica tome conciencia de la gran superioridad del análisis multivariante sobre el análisis univariante. No se describen los detalles matemáticos y algorítmicos porque el articulo está dirigido a problemas genéricos en los que la Quimiometría puede ser aplicada con éxito dentro del campo de la Química Analítica.

  13. 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.

  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. 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.

  16. A hybrid genetic algorithm to optimize simple distillation column sequences

    Institute of Scientific and Technical Information of China (English)

    GAN YongSheng; Andreas Linninger

    2004-01-01

    Based on the principles of Genetic Algorithms (GAs), a hybrid genetic algorithm used to optimize simple distillation column sequences was established. A new data structure, a novel arithmetic crossover operator and a dynamic mutation operator were proposed. Together with the feasibility test of distillation columns, they are capable to obtain the optimum simple column sequence at one time without the limitation of the number of mixture components, ideal or non-ideal mixtures and sloppy or sharp splits. Compared with conventional algorithms, this hybrid genetic algorithm avoids solving complicated nonlinear equations and demands less derivative information and computation time. Result comparison between this genetic algorithm and Underwood method and Doherty method shows that this hybrid genetic algorithm is reliable.

  17. Double Exchange Genetic Algorithm for the Synthesis of Linear Array

    Directory of Open Access Journals (Sweden)

    Zhang Jian-Hua

    2012-01-01

    Full Text Available The development of Synthesis of Linear array put forward higher request for complex optimization solutions. This article improves the basic genetic algorithm according to the traditional genetic algorithm easily prematuring convergence and later evolution slow convergence shortcoming. And then, adopt double exchange operator in reproductive strategies and implement dynamic mutation rate in variation operations. Combined characteristics of guarantee to the population diversity based on fitness shared niche while iteration times exponential diminishing, this article creats niche double exchangegenetic algorithm, and applies in pattern synthesis of homogeneous linear array, and simulates multi-objective complex array problem. The result turns out much better in effectively preventing premature and improving the searching efficiency of genetic algorithm than original genetic algorithm and immune genetic algorithm, what will achieve the broad prospect in the antenna array comprehensive field.

  18. OPTIMIZATION BASED ON LMPROVED REAL—CODED GENETIC ALGORITHM

    Institute of Scientific and Technical Information of China (English)

    ShiYu; YuShenglin

    2002-01-01

    An improved real-coded genetic algorithm is pro-posed for global optimization of functionsl.The new algo-rithm is based om the judgement of the searching perfor-mance of basic real-coded genetic algorithm.The opera-tions of basic real-coded genetic algorithm are briefly dis-cussed and selected.A kind of chaos sequence is described in detail and added in the new algorithm ad a disturbance factor.The strategy of field partition is also used to im-prove the strcture of the new algorithm.Numerical ex-periment shows that the mew genetic algorithm can find the global optimum of complex funtions with satistaiting precision.

  19. An Improved Genetic Algorithm with Quasi-Gradient Crossover

    Institute of Scientific and Technical Information of China (English)

    Xiao-Ling Zhang; Li Du; Guang-Wei Zhang; Qiang Miao; Zhong-Lai Wang

    2008-01-01

    The convergence of genetic algorithm is mainly determined by its core operation crossover operation. When the objective function is a multiple hump function, traditional genetic algorithms are easily trapped into local optimum, which is called premature conver gence. In this paper, we propose a new genetic algorithm with improved arithmetic crossover operation based on gradient method. This crossover operation can generate offspring along quasi-gradient direction which is the Steepest descent direction of the value of objective function. The selection operator is also simplified, every individual in the population is given an opportunity to get evolution to avoid complicated selection algorithm. The adaptive mutation operator and the elitist strategy are also applied in this algorithm. The case 4 indicates this algorithm can faster converge to the global optimum and is more stable than the conventional genetic algorithms.

  20. Selection of individual features of a speech signal using genetic algorithms

    Directory of Open Access Journals (Sweden)

    Kamil Kamiński

    2016-03-01

    Full Text Available The paper presents an automatic speaker’s recognition system, implemented in the Matlab environment, and demonstrates how to achieve and optimize various elements of the system. The main emphasis was put on features selection of a speech signal using a genetic algorithm which takes into account synergy of features. The results of optimization of selected elements of a classifier have been also shown, including the number of Gaussian distributions used to model each of the voices. In addition, for creating voice models, a universal voice model has been used.[b]Keywords[/b]: biometrics, automatic speaker recognition, genetic algorithms, feature selection

  1. Hybrid Genetic Algorithm with PSO Effect for Combinatorial Optimisation Problems

    Directory of Open Access Journals (Sweden)

    M. H. Mehta

    2012-12-01

    Full Text Available In engineering field, many problems are hard to solve in some definite interval of time. These problems known as “combinatorial optimisation problems” are of the category NP. These problems are easy to solve in some polynomial time when input size is small but as input size grows problems become toughest to solve in some definite interval of time. Long known conventional methods are not able to solve the problems and thus proper heuristics is necessary. Evolutionary algorithms based on behaviours of different animals and species have been invented and studied for this purpose. Genetic Algorithm is considered a powerful algorithm for solving combinatorial optimisation problems. Genetic algorithms work on these problems mimicking the human genetics. It follows principle of “survival of the fittest” kind of strategy. Particle swarm optimisation is a new evolutionary approach that copies behaviour of swarm in nature. However, neither traditional genetic algorithms nor particle swarm optimisation alone has been completely successful for solving combinatorial optimisation problems. Here a hybrid algorithm is proposed in which strengths of both algorithms are merged and performance of proposed algorithm is compared with simple genetic algorithm. Results show that proposed algorithm works definitely better than the simple genetic algorithm.

  2. Progression of patterns (POP): a machine classifier algorithm to identify glaucoma progression in visual fields.

    Science.gov (United States)

    Goldbaum, Michael H; Lee, Intae; Jang, Giljin; Balasubramanian, Madhusudhanan; Sample, Pamela A; Weinreb, Robert N; Liebmann, Jeffrey M; Girkin, Christopher A; Anderson, Douglas R; Zangwill, Linda M; Fredette, Marie-Josee; Jung, Tzyy-Ping; Medeiros, Felipe A; Bowd, Christopher

    2012-09-25

    We evaluated Progression of Patterns (POP) for its ability to identify progression of glaucomatous visual field (VF) defects. POP uses variational Bayesian independent component mixture model (VIM), a machine learning classifier (MLC) developed previously. VIM separated Swedish Interactive Thresholding Algorithm (SITA) VFs from a set of 2,085 normal and glaucomatous eyes into nine axes (VF patterns): seven glaucomatous. Stable glaucoma was simulated in a second set of 55 patient eyes with five VFs each, collected within four weeks. A third set of 628 eyes with 4,186 VFs (mean ± SD of 6.7 ± 1.7 VFs over 4.0 ± 1.4 years) was tested for progression. Tested eyes were placed into suspect and glaucoma categories at baseline, based on VFs and disk stereoscopic photographs; a subset of eyes had stereophotographic evidence of progressive glaucomatous optic neuropathy (PGON). Each sequence of fields was projected along seven VIM glaucoma axes. Linear regression (LR) slopes generated from projections onto each axis yielded a degree of confidence (DOC) that there was progression. At 95% specificity, progression cutoffs were established for POP, visual field index (VFI), and mean deviation (MD). Guided progression analysis (GPA) was also compared. POP identified a statistically similar number of eyes (P > 0.05) as progressing compared with VFI, MD, and GPA in suspects (3.8%, 2.7%, 5.6%, and 2.9%, respectively), and more eyes than GPA (P = 0.01) in glaucoma (16.0%, 15.3%, 12.0%, and 7.3%, respectively), and more eyes than GPA (P = 0.05) in PGON eyes (26.3%, 23.7%, 27.6%, and 14.5%, respectively). POP, with its display of DOC of progression and its identification of progressing VF defect pattern, adds to the information available to the clinician for detecting VF progression.

  3. Feature selection using genetic algorithms for fetal heart rate analysis.

    Science.gov (United States)

    Xu, Liang; Redman, Christopher W G; Payne, Stephen J; Georgieva, Antoniya

    2014-07-01

    The fetal heart rate (FHR) is monitored on a paper strip (cardiotocogram) during labour to assess fetal health. If necessary, clinicians can intervene and assist with a prompt delivery of the baby. Data-driven computerized FHR analysis could help clinicians in the decision-making process. However, selecting the best computerized FHR features that relate to labour outcome is a pressing research problem. The objective of this study is to apply genetic algorithms (GA) as a feature selection method to select the best feature subset from 64 FHR features and to integrate these best features to recognize unfavourable FHR patterns. The GA was trained on 404 cases and tested on 106 cases (both balanced datasets) using three classifiers, respectively. Regularization methods and backward selection were used to optimize the GA. Reasonable classification performance is shown on the testing set for the best feature subset (Cohen's kappa values of 0.45 to 0.49 using different classifiers). This is, to our knowledge, the first time that a feature selection method for FHR analysis has been developed on a database of this size. This study indicates that different FHR features, when integrated, can show good performance in predicting labour outcome. It also gives the importance of each feature, which will be a valuable reference point for further studies.

  4. Bearing Fault Detection Using Artificial Neural Networks and Genetic Algorithm

    Directory of Open Access Journals (Sweden)

    Samanta B

    2004-01-01

    Full Text Available A study is presented to compare the performance of bearing fault detection using three types of artificial neural networks (ANNs, namely, multilayer perceptron (MLP, radial basis function (RBF network, and probabilistic neural network (PNN. The time domain vibration signals of a rotating machine with normal and defective bearings are processed for feature extraction. The extracted features from original and preprocessed signals are used as inputs to all three ANN classifiers: MLP, RBF, and PNN for two-class (normal or fault recognition. The characteristic parameters like number of nodes in the hidden layer of MLP and the width of RBF, in case of RBF and PNN along with the selection of input features, are optimized using genetic algorithms (GA. For each trial, the ANNs are trained with a subset of the experimental data for known machine conditions. The ANNs are tested using the remaining set of data. The procedure is illustrated using the experimental vibration data of a rotating machine with and without bearing faults. The results show the relative effectiveness of three classifiers in detection of the bearing condition.

  5. A review and experimental study on application of classifiers and evolutionary algorithms in EEG based brain-machine interface systems.

    Science.gov (United States)

    Tahernezhad-Javazm, Farajollah; Azimirad, Vahid; Shoaran, Maryam

    2017-07-18

    Considering the importance and the near future development of noninvasive Brain-Machine Interface (BMI) systems, this paper presents a comprehensive theoretical-experimental survey on the classification and evolutionary methods for BMI-based systems in which EEG signals are used. The paper is divided into two main parts. In the first part a wide range of different types of the base and combinatorial classifiers including boosting and bagging classifiers and also evolutionary algorithms are reviewed and investigated. In the second part, these classifiers and evolutionary algorithms are assessed and compared based on two types of relatively widely used BMI systems, that is, Sensory Motor Rhythm-BMI (SMR-BMI) and Event Related Potentials-BMI (ERPs-BMI). Moreover, in the second part, some of the improved evolutionary algorithms as well as bi-objective algorithms are experimentally assessed and compared. In this study two databases are used, and cross-validation accuracy (CVA) and stability to data volume (SDV) are considered as the evaluation criteria for the classifiers. According to the experimental results on both databases, regarding the base classifiers, LDA (Linear Discriminant Analysis) and SVM (Support Vector Machines) with respect to CVA evaluation metric, and NB (Naive Bayes) with respect to SDV demonstrated the best performances. Among the combinatorial classifiers, four classifiers Bagg-DT (Bagging Decision Tree), LogitBoost, and GentleBoost with respect to CVA, and Bagging-LR (Bagging Logistic Regression) and AdaBoost (Adaptive Boosting) with respect to SDV had the best performances. Finally, regarding the evolutionary algorithms, single-objective IWO (Invasive Weed Optimization) and bi-objective NSIWO (Nondominated Sorting IWO) algorithms demonstrated the best performances. We present a general survey on the base and the combinatorial classification methods for EEG signals (sensory motor rhythm and event related potentials) as well as their optimization

  6. 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.

  7. 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

  8. Semiclassical genetic algorithm with quantum crossover and mutation operations

    CERN Document Server

    SaiToh, Akira; Nakahara, Mikio

    2012-01-01

    In order for finding a good individual for a given fitness function in the context of evolutionary computing, we introduce a novel semiclassical quantum genetic algorithm. It has both of quantum crossover and quantum mutation procedures unlike conventional quantum genetic algorithms. A complexity analysis shows a certain improvement over its classical counterpart.

  9. FEATURE SELECTION USING GENETIC ALGORITHMS FOR HANDWRITTEN CHARACTER RECOGNITION

    NARCIS (Netherlands)

    Kim, G.; Kim, S.

    2004-01-01

    A feature selection method using genetic algorithms which are suitable means for selecting appropriate set of features from ones with huge dimension is proposed. SGA (Simple Genetic Algorithm) and its modified methods are applied to improve the recognition speed as well as the recognition accuracy.

  10. Genetic Algorithms in Dynamical Systems Optimisation and Adaptation

    NARCIS (Netherlands)

    Reus, N.M. de; Visser, E.K.; Bruggeman, B.

    1998-01-01

    Both in the design of dynamical systems, ranging from control systems to state estimators as in the adaptation of these systems the use of genetic algorithms is worth studying. This paper presents some approaches for using genetic algorithms in dynamical systems. The layouts and specific uses are di

  11. 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...

  12. 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…

  13. 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...

  14. 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

  15. 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.

  16. Selfish Gene Algorithm Vs Genetic Algorithm: A Review

    Science.gov (United States)

    Ariff, Norharyati Md; Khalid, Noor Elaiza Abdul; Hashim, Rathiah; Noor, Noorhayati Mohamed

    2016-11-01

    Evolutionary algorithm is one of the algorithms inspired by the nature. Within little more than a decade hundreds of papers have reported successful applications of EAs. In this paper, the Selfish Gene Algorithms (SFGA), as one of the latest evolutionary algorithms (EAs) inspired from the Selfish Gene Theory which is an interpretation of Darwinian Theory ideas from the biologist Richards Dawkins on 1989. In this paper, following a brief introduction to the Selfish Gene Algorithm (SFGA), the chronology of its evolution is presented. It is the purpose of this paper is to present an overview of the concepts of Selfish Gene Algorithm (SFGA) as well as its opportunities and challenges. Accordingly, the history, step involves in the algorithm are discussed and its different applications together with an analysis of these applications are evaluated.

  17. Topology control based on quantum genetic algorithm in sensor networks

    Institute of Scientific and Technical Information of China (English)

    SUN Lijuan; GUO Jian; LU Kai; WANG Ruchuan

    2007-01-01

    Nowadays,two trends appear in the application of sensor networks in which both multi-service and quality of service (QoS)are supported.In terms of the goal of low energy consumption and high connectivity,the control on topology is crucial.The algorithm of topology control based on quantum genetic algorithm in sensor networks is proposed.An advantage of the quantum genetic algorithm over the conventional genetic algorithm is demonstrated in simulation experiments.The goals of high connectivity and low consumption of energy are reached.

  18. Global annealing genetic algorithm and its convergence analysis

    Institute of Scientific and Technical Information of China (English)

    张讲社; 徐宗本; 梁怡

    1997-01-01

    A new selection mechanism termed global annealing selection (GAnS) is proposed for the genetic algorithm. It is proved that the GAnS genetic algorithm converges to the global optimums if and only if the parents are allowed to compete for reproduction, and that the variance of population’s fitness can be used as a natural stopping criterion. Numerical simulations show that the new algorithm has stronger ability to escape from local maximum and converges more rapidly than canonical genetic algorithm.

  19. 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.

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

    Directory of Open Access Journals (Sweden)

    Kong Haipeng

    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.

  1. 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.

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

    Institute of Scientific and Technical Information of China (English)

    Kong Haipeng; Li Ni; Shen Yuzhong

    2015-01-01

    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 con-vergence 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.

  3. 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

  4. 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

  5. Genetic Algorithm Based Proportional Integral Controller Design for Induction Motor

    Directory of Open Access Journals (Sweden)

    Mohanasundaram Kuppusamy

    2011-01-01

    Full Text Available Problem statement: This study has expounded the application of evolutionary computation method namely Genetic Algorithm (GA for estimation of feedback controller parameters for induction motor. GA offers certain advantages such as simple computational steps, derivative free optimization, reduced number of iterations and assured near global optima. The development of the method is well documented and computed and measured results are presented. Approach: The design of PI controller parameter for three phase induction motor drives was done using Genetic Algorithm. The objective function of motor current reduction, using PI controller, at starting is formulated as an optimization problem and solved with Genetic Algorithm. Results: The results showed the selected values of PI controller parameter using genetic algorithm approach, with objective of induction motor starting current reduction. Conclusions/Recommendation: The results proved the robustness and easy implementation of genetic algorithm selection of PI parameters for induction motor starting.

  6. Scrutinizing an algorithmic technique: the Bayes classifier as interested reading of reality

    NARCIS (Netherlands)

    Rieder, B.

    2017-01-01

    This paper outlines the notion of ‘algorithmic technique’ as a middle ground between concrete, implemented algorithms and the broader study and theorization of software. Algorithmic techniques specify principles and methods for doing things in the medium of software and they thus constitute units of

  7. Efficient Genetic Algorithm sets for optimizing constrained building design problem

    National Research Council Canada - National Science Library

    Wright, Jonathan; Alajmi, Ali

    2016-01-01

    .... This requires trying large possible solutions which need heuristic optimization algorithms. A comparison between several heuristic optimization algorithms showed that Genetic Algorithm (GA) is robust on getting the optimum(s) simulation ( Wetter and Wright, 2004; Brownlee et al., 2011; Bichiou and Krarti, 2011; Sahu et al., 2012 ) while the building simulat...

  8. Seasonal Time Series Analysis Based on Genetic Algorithm

    Institute of Scientific and Technical Information of China (English)

    2007-01-01

    Pattern discovery from the seasonal time-series is of importance. Traditionally, most of the algorithms of pattern discovery in time series are similar. A novel mode of time series is proposed which integrates the Genetic Algorithm (GA) for the actual problem. The experiments on the electric power yield sequence models show that this algorithm is practicable and effective.

  9. New Iris Localization Method Based on Chaos Genetic Algorithm

    Institute of Scientific and Technical Information of China (English)

    Jia Dongli; Muhammad Khurram Khan; Zhang Jiashu

    2005-01-01

    This paper present a new method based on Chaos Genetic Algorithm (CGA) to localize the human iris in a given image. First, the iris image is preprocessed to estimate the range of the iris localization, and then CGA is used to extract the boundary of the iris. Simulation results show that the proposed algorithms is efficient and robust, and can achieve sub pixel precision. Because Genetic Algorithms (GAs) can search in a large space, the algorithm does not need accurate estimation of iris center for subsequent localization, and hence can lower the requirement for original iris image processing. On this point, the present localization algirithm is superior to Daugmans algorithm.

  10. Transonic Wing Shape Optimization Using a Genetic Algorithm

    Science.gov (United States)

    Holst, Terry L.; Pulliam, Thomas H.; Kwak, Dochan (Technical Monitor)

    2002-01-01

    A method for aerodynamic shape optimization based on a genetic algorithm approach is demonstrated. The algorithm is coupled with a transonic full potential flow solver and is used to optimize the flow about transonic wings including multi-objective solutions that lead to the generation of pareto fronts. The results indicate that the genetic algorithm is easy to implement, flexible in application and extremely reliable.

  11. Higher-Order Quantum-Inspired Genetic Algorithms

    OpenAIRE

    Nowotniak, Robert; Kucharski, Jacek

    2014-01-01

    This paper presents a theory and an empirical evaluation of Higher-Order Quantum-Inspired Genetic Algorithms. Fundamental notions of the theory have been introduced, and a novel Order-2 Quantum-Inspired Genetic Algorithm (QIGA2) has been presented. Contrary to all QIGA algorithms which represent quantum genes as independent qubits, in higher-order QIGAs quantum registers are used to represent genes strings which allows modelling of genes relations using quantum phenomena. Performance comparis...

  12. 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.

  13. A Hybrid Algorithm for Satellite Data Transmission Schedule Based on Genetic Algorithm

    Institute of Scientific and Technical Information of China (English)

    LI Yun-feng; WU Xiao-yue

    2008-01-01

    A hybrid scheduling algorithm based on genetic algorithm is proposed in this paper for reconnaissance satellite data transmission. At first, based on description of satellite data transmission request, satellite data transmission task modal and satellite data transmission scheduling problem model are established. Secondly, the conflicts in scheduling are discussed. According to the meaning of possible conflict, the method to divide possible conflict task set is given. Thirdly, a hybrid algorithm which consists of genetic algorithm and heuristic information is presented. The heuristic information comes from two concepts, conflict degree and conflict number. Finally, an example shows the algorithm's feasibility and performance better than other traditional algorithms.

  14. Genetic algorithm-based wide-band deterministic maximum likelihood direction finding algorithm

    Institute of Scientific and Technical Information of China (English)

    2005-01-01

    The wide-band direction finding is one of hit and difficult task in array signal processing. This paper generalizes narrow-band deterministic maximum likelihood direction finding algorithm to the wideband case, and so constructions an object function, then utilizes genetic algorithm for nonlinear global optimization. Direction of arrival is estimated without preprocessing of array data and so the algorithm eliminates the effect of pre-estimate on the final estimation. The algorithm is applied on uniform linear array and extensive simulation results prove the efficacy of the algorithm. In the process of simulation, we obtain the relation between estimation error and parameters of genetic algorithm.

  15. Investigation of Web Mining Optimization Using Microbial Genetic Algorithm

    Directory of Open Access Journals (Sweden)

    Dipali Tungar

    2014-02-01

    Full Text Available In today's modern internet era peopleneed searching on the web and finding relevant information on the web to be efficient and fast. But traditional search engines like Google suppose to be more intelligent, still use the traditional crawling algorithms to find data relevant to the search query. But most of the times it returns irrelevant data as well which becomes confusing for the user. In a normal XML data the user inputs the search query in terms of a keyword or a question and the answer to the search query should be more precise and more relevant. So, using the traditional crawling algorithms over XML data would lead to irrelevant results. Genetic algorithms are the modern algorithms which replicates the Darwinian theory of the natural evolution. The genetic algorithms are best suited for the traditional search problem as the genetic algorithms always tend to return quality as solution for any domain data. It would be a good approach to investigate how the genetic algorithms would be suitable for the search over the XML data of different domains. So, this system implements a steady state tournament selection Microbial Genetic Algorithm over the XML data of the different domains. This would be an investigation of how the genetic algorithm would return accurate results over XML data of different domains.

  16. Genetic Algorithms for Satellite Scheduling Problems

    Directory of Open Access Journals (Sweden)

    Fatos Xhafa

    2012-01-01

    Full Text Available Recently there has been a growing interest in mission operations scheduling problem. The problem, in a variety of formulations, arises in management of satellite/space missions requiring efficient allocation of user requests to make possible the communication between operations teams and spacecraft systems. Not only large space agencies, such as ESA (European Space Agency and NASA, but also smaller research institutions and universities can establish nowadays their satellite mission, and thus need intelligent systems to automate the allocation of ground station services to space missions. In this paper, we present some relevant formulations of the satellite scheduling viewed as a family of problems and identify various forms of optimization objectives. The main complexities, due highly constrained nature, windows accessibility and visibility, multi-objectives and conflicting objectives are examined. Then, we discuss the resolution of the problem through different heuristic methods. In particular, we focus on the version of ground station scheduling, for which we present computational results obtained with Genetic Algorithms using the STK simulation toolkit.

  17. 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.

  18. 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.

  19. 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.

  20. Robot path planning using a genetic algorithm

    Science.gov (United States)

    Cleghorn, Timothy F.; Baffes, Paul T.; Wang, Liu

    1988-01-01

    Robot path planning can refer either to a mobile vehicle such as a Mars Rover, or to an end effector on an arm moving through a cluttered workspace. In both instances there may exist many solutions, some of which are better than others, either in terms of distance traversed, energy expended, or joint angle or reach capabilities. A path planning program has been developed based upon a genetic algorithm. This program assumes global knowledge of the terrain or workspace, and provides a family of good paths between the initial and final points. Initially, a set of valid random paths are constructed. Successive generations of valid paths are obtained using one of several possible reproduction strategies similar to those found in biological communities. A fitness function is defined to describe the goodness of the path, in this case including length, slope, and obstacle avoidance considerations. It was found that with some reproduction strategies, the average value of the fitness function improved for successive generations, and that by saving the best paths of each generation, one could quite rapidly obtain a collection of good candidate solutions.

  1. Multiobjective Genetic Algorithm applied to dengue control.

    Science.gov (United States)

    Florentino, Helenice O; Cantane, Daniela R; Santos, Fernando L P; Bannwart, Bettina F

    2014-12-01

    Dengue fever is an infectious disease caused by a virus of the Flaviridae family and transmitted to the person by a mosquito of the genus Aedes aegypti. This disease has been a global public health problem because a single mosquito can infect up to 300 people and between 50 and 100 million people are infected annually on all continents. Thus, dengue fever is currently a subject of research, whether in the search for vaccines and treatments for the disease or efficient and economical forms of mosquito control. The current study aims to study techniques of multiobjective optimization to assist in solving problems involving the control of the mosquito that transmits dengue fever. The population dynamics of the mosquito is studied in order to understand the epidemic phenomenon and suggest strategies of multiobjective programming for mosquito control. A Multiobjective Genetic Algorithm (MGA_DENGUE) is proposed to solve the optimization model treated here and we discuss the computational results obtained from the application of this technique.

  2. Application of Genetic Algorithm in the Layout of Fixture Components

    Institute of Scientific and Technical Information of China (English)

    焦黎; 孙厚芳

    2003-01-01

    Automation in the layout of fixture components is important to achieve efficiency and flexibility in computer aided fixture design. Based on basic genetic algorithm and particulars of different fixture components, a method of layout space division is presented. Such techniques as suitable crossover rate, mutation rate and selection arithmetic element are adopted in the genetic operation. The results show that genetic algorithm can effectively be applied in the automatic layout of fixture components.

  3. Key Frames Extraction Based on the Improved Genetic Algorithm

    Institute of Scientific and Technical Information of China (English)

    ZHOU Dong-sheng; JIANG Wei; YI Peng-fei; LIURui

    2014-01-01

    In order toovercomethe poor local search ability of genetic algorithm, resulting in the basic genetic algorithm is time-consuming, and low search abilityin the late evolutionary, we use thegray coding instead ofbinary codingatthebeginning of the coding;we use multi-point crossoverto replace the originalsingle-point crossoveroperation.Finally, theexperimentshows that the improved genetic algorithmnot only has a strong search capability, but also thestability has been effectively improved.

  4. Coordinating Exploration and Exploitation To Construct Genetic Algorithms

    Institute of Scientific and Technical Information of China (English)

    江瑞; 罗予频; 胡东成; 司徒国业

    2002-01-01

    A new genetic algorithm is proposed based on the careful coordination of the exploration in the solution space of the given problem and the exploitation of the information from the previous search. In the new algorithm architecture, the population in each generation consists of three sub-populations: a preserved part, a reproduced part, and a randomized part. Two parameters are incorporated into the algorithm to efficiently control the percentage of each sub-population to achieve good balance between the exploration and exploitation processes during the optimization. By modeling the algorithm as a homogeneous finite Markov chain, the new genetic algorithm is shown to converge towards the global optimum of the problem at hand. Experiments were designed to test the algorithm using the Rastrigin function, the Griewangk function, and the Schaffer function. Data analyses using the average success ratio, the average objective calculating number, the average first passage time to solution, and the standard deviation of the first passage time were compared with those of the canonical genetic algorithm, the elitist genetic algorithm, and the steady genetic algorithm. The results show strong evidence that our algorithm is superior in performance in terms of economy, robustness and efficiency.

  5. Supervised Machine Learning Algorithms Can Classify Open-Text Feedback of Doctor Performance With Human-Level Accuracy.

    Science.gov (United States)

    Gibbons, Chris; Richards, Suzanne; Valderas, Jose Maria; Campbell, John

    2017-03-15

    Machine learning techniques may be an effective and efficient way to classify open-text reports on doctor's activity for the purposes of quality assurance, safety, and continuing professional development. The objective of the study was to evaluate the accuracy of machine learning algorithms trained to classify open-text reports of doctor performance and to assess the potential for classifications to identify significant differences in doctors' professional performance in the United Kingdom. We used 1636 open-text comments (34,283 words) relating to the performance of 548 doctors collected from a survey of clinicians' colleagues using the General Medical Council Colleague Questionnaire (GMC-CQ). We coded 77.75% (1272/1636) of the comments into 5 global themes (innovation, interpersonal skills, popularity, professionalism, and respect) using a qualitative framework. We trained 8 machine learning algorithms to classify comments and assessed their performance using several training samples. We evaluated doctor performance using the GMC-CQ and compared scores between doctors with different classifications using t tests. Individual algorithm performance was high (range F score=.68 to .83). Interrater agreement between the algorithms and the human coder was highest for codes relating to "popular" (recall=.97), "innovator" (recall=.98), and "respected" (recall=.87) codes and was lower for the "interpersonal" (recall=.80) and "professional" (recall=.82) codes. A 10-fold cross-validation demonstrated similar performance in each analysis. When combined together into an ensemble of multiple algorithms, mean human-computer interrater agreement was .88. Comments that were classified as "respected," "professional," and "interpersonal" related to higher doctor scores on the GMC-CQ compared with comments that were not classified (Pdoctors who were rated as popular or innovative and those who were not rated at all (P>.05). Machine learning algorithms can classify open-text feedback

  6. Feature selection using genetic algorithm for breast cancer diagnosis: experiment on three different datasets

    Science.gov (United States)

    Aalaei, Shokoufeh; Shahraki, Hadi; Rowhanimanesh, Alireza; Eslami, Saeid

    2016-01-01

    Objective(s): This study addresses feature selection for breast cancer diagnosis. The present process uses a wrapper approach using GA-based on feature selection and PS-classifier. The results of experiment show that the proposed model is comparable to the other models on Wisconsin breast cancer datasets. Materials and Methods: To evaluate effectiveness of proposed feature selection method, we employed three different classifiers artificial neural network (ANN) and PS-classifier and genetic algorithm based classifier (GA-classifier) on Wisconsin breast cancer datasets include Wisconsin breast cancer dataset (WBC), Wisconsin diagnosis breast cancer (WDBC), and Wisconsin prognosis breast cancer (WPBC). Results: For WBC dataset, it is observed that feature selection improved the accuracy of all classifiers expect of ANN and the best accuracy with feature selection achieved by PS-classifier. For WDBC and WPBC, results show feature selection improved accuracy of all three classifiers and the best accuracy with feature selection achieved by ANN. Also specificity and sensitivity improved after feature selection. Conclusion: The results show that feature selection can improve accuracy, specificity and sensitivity of classifiers. Result of this study is comparable with the other studies on Wisconsin breast cancer datasets. PMID:27403253

  7. Feature selection using genetic algorithm for breast cancer diagnosis: experiment on three different datasets

    Directory of Open Access Journals (Sweden)

    Shokoufeh Aalaei

    2016-05-01

    Full Text Available Objective(s: This study addresses feature selection for breast cancer diagnosis. The present process uses a wrapper approach using GA-based on feature selection and PS-classifier. The results of experiment show that the proposed model is comparable to the other models on Wisconsin breast cancer datasets. Materials and Methods: To evaluate effectiveness of proposed feature selection method, we employed three different classifiers artificial neural network (ANN and PS-classifier and genetic algorithm based classifier (GA-classifier on Wisconsin breast cancer datasets include Wisconsin breast cancer dataset (WBC, Wisconsin diagnosis breast cancer (WDBC, and Wisconsin prognosis breast cancer (WPBC. Results: For WBC dataset, it is observed that feature selection improved the accuracy of all classifiers expect of ANN and the best accuracy with feature selection achieved by PS-classifier. For WDBC and WPBC, results show feature selection improved accuracy of all three classifiers and the best accuracy with feature selection achieved by ANN. Also specificity and sensitivity improved after feature selection. Conclusion: The results show that feature selection can improve accuracy, specificity and sensitivity of classifiers. Result of this study is comparable with the other studies on Wisconsin breast cancer datasets.

  8. A genetic algorithm for solving supply chain network design model

    Science.gov (United States)

    Firoozi, Z.; Ismail, N.; Ariafar, S. H.; Tang, S. H.; Ariffin, M. K. M. A.

    2013-09-01

    Network design is by nature costly and optimization models play significant role in reducing the unnecessary cost components of a distribution network. This study proposes a genetic algorithm to solve a distribution network design model. The structure of the chromosome in the proposed algorithm is defined in a novel way that in addition to producing feasible solutions, it also reduces the computational complexity of the algorithm. Computational results are presented to show the algorithm performance.

  9. Successive Projections Algorithm-Multivariable Linear Regression Classifier for the Detection of Contaminants on Chicken Carcasses in Hyperspectral Images

    Science.gov (United States)

    Wu, W.; Chen, G. Y.; Kang, R.; Xia, J. C.; Huang, Y. P.; Chen, K. J.

    2017-07-01

    During slaughtering and further processing, chicken carcasses are inevitably contaminated by microbial pathogen contaminants. Due to food safety concerns, many countries implement a zero-tolerance policy that forbids the placement of visibly contaminated carcasses in ice-water chiller tanks during processing. Manual detection of contaminants is labor consuming and imprecise. Here, a successive projections algorithm (SPA)-multivariable linear regression (MLR) classifier based on an optimal performance threshold was developed for automatic detection of contaminants on chicken carcasses. Hyperspectral images were obtained using a hyperspectral imaging system. A regression model of the classifier was established by MLR based on twelve characteristic wavelengths (505, 537, 561, 562, 564, 575, 604, 627, 656, 665, 670, and 689 nm) selected by SPA , and the optimal threshold T = 1 was obtained from the receiver operating characteristic (ROC) analysis. The SPA-MLR classifier provided the best detection results when compared with the SPA-partial least squares (PLS) regression classifier and the SPA-least squares supported vector machine (LS-SVM) classifier. The true positive rate (TPR) of 100% and the false positive rate (FPR) of 0.392% indicate that the SPA-MLR classifier can utilize spatial and spectral information to effectively detect contaminants on chicken carcasses.

  10. Error-free image compression algorithm using classifying-sequencing techniques.

    Science.gov (United States)

    He, J D; Dereniak, E L

    1992-05-10

    The development of a new error-free digital image compression algorithm is discussed. Without the help of any statistics information of the images being processed, this algorithm achieves average bits-per-word ratios near the entropy of the neighboring pixel differences. Because this algorithm does not involve statistical modeling, generation of a code book, or long integer-floating point arithmetics, it is simpler and, therefore, faster than the studied statistics codes, such as the Huffman code or the arithmetic code.

  11. 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.

  12. A Software Pattern of the Genetic Algorithm -a Study on Reusable Object Model of Genetic Algorithm

    Institute of Scientific and Technical Information of China (English)

    2001-01-01

    The Genetic Algorithm (GA) has been a pop research field, butthere is little concern on GA in view of Software Engineering and this result in a serie s of problems. In this paper, we extract a GA's software pattern, draw a model d iagram of the reusable objects, analyze the advantages and disadvantages of the pattern, and give a sample code at the end. We are then able to improve the reus ability and expansibility of GA. The results make it easier to program a new GA code by using some existing successful operators, thereby reducing the difficult ies and workload of programming a GA's code, and facilitate the GA application.

  13. An adaptive genetic algorithm for solving bilevel linear programming problem

    Institute of Scientific and Technical Information of China (English)

    2007-01-01

    Bilevel linear programming, which consists of the objective functions of the upper level and lower level, is a useful tool for modeling decentralized decision problems.Various methods are proposed for solving this problem. Of all the algorithms, the genetic algorithm is an alternative to conventional approaches to find the solution of the bilevel linear programming. In this paper, we describe an adaptive genetic algorithm for solving the bilevel linear programming problem to overcome the difficulty of determining the probabilities of crossover and mutation. In addition, some techniques are adopted not only to deal with the difficulty that most of the chromosomes may be infeasible in solving constrained optimization problem with genetic algorithm but also to improve the efficiency of the algorithm. The performance of this proposed algorithm is illustrated by the examples from references.

  14. Heterogeneous Ensemble Combination Search Using Genetic Algorithm for Class Imbalanced Data Classification.

    Science.gov (United States)

    Haque, Mohammad Nazmul; Noman, Nasimul; Berretta, Regina; Moscato, Pablo

    2016-01-01

    Classification of datasets with imbalanced sample distributions has always been a challenge. In general, a popular approach for enhancing classification performance is the construction of an ensemble of classifiers. However, the performance of an ensemble is dependent on the choice of constituent base classifiers. Therefore, we propose a genetic algorithm-based search method for finding the optimum combination from a pool of base classifiers to form a heterogeneous ensemble. The algorithm, called GA-EoC, utilises 10 fold-cross validation on training data for evaluating the quality of each candidate ensembles. In order to combine the base classifiers decision into ensemble's output, we used the simple and widely used majority voting approach. The proposed algorithm, along with the random sub-sampling approach to balance the class distribution, has been used for classifying class-imbalanced datasets. Additionally, if a feature set was not available, we used the (α, β) - k Feature Set method to select a better subset of features for classification. We have tested GA-EoC with three benchmarking datasets from the UCI-Machine Learning repository, one Alzheimer's disease dataset and a subset of the PubFig database of Columbia University. In general, the performance of the proposed method on the chosen datasets is robust and better than that of the constituent base classifiers and many other well-known ensembles. Based on our empirical study we claim that a genetic algorithm is a superior and reliable approach to heterogeneous ensemble construction and we expect that the proposed GA-EoC would perform consistently in other cases.

  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. A NEW GENETIC SIMULATED ANNEALING ALGORITHM FOR FLOOD ROUTING MODEL

    Institute of Scientific and Technical Information of China (English)

    KANG Ling; WANG Cheng; JIANG Tie-bing

    2004-01-01

    In this paper, a new approach, the Genetic Simulated Annealing (GSA), was proposed for optimizing the parameters in the Muskingum routing model. By integrating the simulated annealing method into the genetic algorithm, the hybrid method could avoid some troubles of traditional methods, such as arduous trial-and-error procedure, premature convergence in genetic algorithm and search blindness in simulated annealing. The principle and implementing procedure of this algorithm were described. Numerical experiments show that the GSA can adjust the optimization population, prevent premature convergence and seek the global optimal result.Applications to the Nanyunhe River and Qingjiang River show that the proposed approach is of higher forecast accuracy and practicability.

  17. Aerodynamic Optimum Design of Transonic Turbine Cascades Using Genetic Algorithms

    Institute of Scientific and Technical Information of China (English)

    1997-01-01

    This paper presents an aerodynamic optimum design method for transonic turbine cascades based on the Genetic Algorithms coupled to the inviscid flow Euler Solver and the boundary-layer calculation.The Genetic Algorithms control the evolution of a population of cascades towards an optimum design.The fitness value of each string is evaluated using the flow solver.The design procedure has been developed and the behavior of the genetic algorithms has been tested.The objective functions of the design examples are the minimum mean-square deviation between the aimed pressure and computed pressure and the minimum amount of user expertise.

  18. Support Vector Machine Optimized by Improved Genetic Algorithm

    Directory of Open Access Journals (Sweden)

    Xiang Chang Sheng

    2013-07-01

    Full Text Available Parameters of support vector machines (SVM which is optimized by standard genetic algorithm is easy to trap into the local minimum, in order to get the optimal parameters of support vector machine, this paper proposed a parameters optimization method for support vector machines based on improved genetic algorithm, the simulation experiment is carried out on 5 benchmark datasets. The simulation show that the proposed method not only can assure the classification precision, but also can reduce training time markedly compared with standard genetic algorithm.

  19. Classifying Microorganisms

    DEFF Research Database (Denmark)

    Sommerlund, Julie

    2006-01-01

    This paper describes the coexistence of two systems for classifying organisms and species: a dominant genetic system and an older naturalist system. The former classifies species and traces their evolution on the basis of genetic characteristics, while the latter employs physiological characteris......This paper describes the coexistence of two systems for classifying organisms and species: a dominant genetic system and an older naturalist system. The former classifies species and traces their evolution on the basis of genetic characteristics, while the latter employs physiological...... and integration possible, the field of molecular biology seems to be overwhelmingly homogeneous, and in need of heterogeneity and conflict to add drive and momentum to the work being carried out. The paper is based on observations of daily life in a molecular microbiology laboratory at the Technical University...

  20. TEXTURE BASED LAND COVER CLASSIFICATION ALGORITHM USING GABOR WAVELET AND ANFIS CLASSIFIER

    Directory of Open Access Journals (Sweden)

    S. Jenicka

    2016-05-01

    Full Text Available Texture features play a predominant role in land cover classification of remotely sensed images. In this study, for extracting texture features from data intensive remotely sensed image, Gabor wavelet has been used. Gabor wavelet transform filters frequency components of an image through decomposition and produces useful features. For classification of fuzzy land cover patterns in the remotely sensed image, Adaptive Neuro Fuzzy Inference System (ANFIS has been used. The strength of ANFIS classifier is that it combines the merits of fuzzy logic and neural network. Hence in this article, land cover classification of remotely sensed image has been performed using Gabor wavelet and ANFIS classifier. The classification accuracy of the classified image obtained is found to be 92.8%.

  1. A New Genetic Algorithm Methodology for Design Optimization of Truss Structures: Bipopulation-Based Genetic Algorithm with Enhanced Interval Search

    Directory of Open Access Journals (Sweden)

    Tugrul Talaslioglu

    2009-01-01

    Full Text Available A new genetic algorithm (GA methodology, Bipopulation-Based Genetic Algorithm with Enhanced Interval Search (BGAwEIS, is introduced and used to optimize the design of truss structures with various complexities. The results of BGAwEIS are compared with those obtained by the sequential genetic algorithm (SGA utilizing a single population, a multipopulation-based genetic algorithm (MPGA proposed for this study and other existing approaches presented in literature. This study has two goals: outlining BGAwEIS's fundamentals and evaluating the performances of BGAwEIS and MPGA. Consequently, it is demonstrated that MPGA shows a better performance than SGA taking advantage of multiple populations, but BGAwEIS explores promising solution regions more efficiently than MPGA by exploiting the feasible solutions. The performance of BGAwEIS is confirmed by better quality degree of its optimal designations compared to algorithms proposed here and described in literature.

  2. 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)

  3. Mobile transporter path planning using a genetic algorithm approach

    Science.gov (United States)

    Baffes, Paul; Wang, Lui

    1988-01-01

    The use of an optimization technique known as a genetic algorithm for solving the mobile transporter path planning problem is investigated. The mobile transporter is a traveling robotic vehicle proposed for the Space Station which must be able to reach any point of the structure autonomously. Specific elements of the genetic algorithm are explored in both a theoretical and experimental sense. Recent developments in genetic algorithm theory are shown to be particularly effective in a path planning problem domain, though problem areas can be cited which require more research. However, trajectory planning problems are common in space systems and the genetic algorithm provides an attractive alternative to the classical techniques used to solve these problems.

  4. PM Synchronous Motor Dynamic Modeling with Genetic Algorithm ...

    African Journals Online (AJOL)

    Adel

    intelligence like neural network, genetic algorithm, etc (El Shahat and El Shewy, ..... maximum power factor has the most powerful effect on all various machine .... Artificial Intelligence, Renewable Energy, Power System, Control Systems, PV ...

  5. Multiscale modeling for classification of SAR imagery using hybrid EM algorithm and genetic algorithm

    Institute of Scientific and Technical Information of China (English)

    Xianbin Wen; Hua Zhang; Jianguang Zhang; Xu Jiao; Lei Wang

    2009-01-01

    A novel method that hybridizes genetic algorithm (GA) and expectation maximization (EM) algorithm for the classification of syn-thetic aperture radar (SAR) imagery is proposed by the finite Gaussian mixtures model (GMM) and multiscale autoregressive (MAR)model. This algorithm is capable of improving the global optimality and consistency of the classification performance. The experiments on the SAR images show that the proposed algorithm outperforms the standard EM method significantly in classification accuracy.

  6. 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.

  7. Equivalent tree representation of electrocardiogram using genetic algorithm.

    Science.gov (United States)

    Kumaravel, N; Rajesh, J; Nithiyanandam, N

    1997-01-01

    Electrocardiogram (ECG) gives the electrical activity of the heart. The number of data points required to represent the ECG signal is reduced by using a complete-tree representation. This reduced data structure (ECG Tree) is obtained by fitting the ECG signal in a grid structure consisting of both horizontal and vertical lines. The leaf nodes are the points where the vertical grid lines intersect with the ECG signal. These leaf nodes now form the features of the ECG signal. Some of these leaf nodes may be redundant and hence the reduction in the number of leaf nodes and thus optimization of the tree (equivalent tree) is done using a novel technique based on the Genetic Algorithm (GA). In this work, the equivalent tree is formed using GA consisting of four stages. First, from the group of generated leaf nodes various combinations of strings are constructed to form the population. Second, the fitness function is taken as the measure of the vertical distances between two neighbouring leaf nodes in order to evaluate the population with respect to their fitness values. Third, the selection procedure is used to give offsprings based on an assigned threshold value. Finally, crossover and mutation operations are performed repeatedly till an optimized population is obtained. The optimal nodes represent the equivalent tree. The Backpropagation Neural Network as a classifier is used to test the efficacy of the GA in this optimization problem.

  8. Genetic-Algorithm Tool For Search And Optimization

    Science.gov (United States)

    Wang, Lui; Bayer, Steven

    1995-01-01

    SPLICER computer program used to solve search and optimization problems. Genetic algorithms adaptive search procedures (i.e., problem-solving methods) based loosely on processes of natural selection and Darwinian "survival of fittest." Algorithms apply genetically inspired operators to populations of potential solutions in iterative fashion, creating new populations while searching for optimal or nearly optimal solution to problem at hand. Written in Think C.

  9. Building Blocks Propagation in Quantum-Inspired Genetic Algorithm

    OpenAIRE

    Nowotniak, Robert; Kucharski, Jacek

    2010-01-01

    This paper presents an analysis of building blocks propagation in Quantum-Inspired Genetic Algorithm, which belongs to a new class of metaheuristics drawing their inspiration from both biological evolution and unitary evolution of quantum systems. The expected number of quantum chromosomes matching a schema has been analyzed and a random variable corresponding to this issue has been introduced. The results have been compared with Simple Genetic Algorithm. Also, it has been presented how selec...

  10. SNMP Based Network Optimization Technique Using Genetic Algorithms

    Directory of Open Access Journals (Sweden)

    M. Mohamed Surputheen

    2012-03-01

    Full Text Available Genetic Algorithms (GAs has innumerable applications through the optimization techniques and network optimization is one of them. SNMP (Simple Network Management Protocol is used as the basic network protocol for monitoring the network activities health of the systems. This paper deals with adding Intelligence to the various aspects of SNMP by adding optimization techniques derived out of genetic algorithms, which enhances the performance of SNMP processes like routing.

  11. 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.

  12. Optimal Design of Materials for DJMP Based on Genetic Algorithm

    Institute of Scientific and Technical Information of China (English)

    FENG Zhong-ren; WANG Xiong-jiang

    2004-01-01

    The genetic algorithm was used in optimal design of deep jet method pile. The cost of deep jetmethod pile in one unit area of foundation was taken as the objective function. All the restrains were listed followingthe corresponding specification. Suggestions were proposed and the modified. The real-coded Genetic Algorithm wasgiven to deal with the problems of excessive computational cost and premature convergence. Software system of opti-mal design of deep jet method pile was developed.

  13. Parallel Genetic Algorithm Based on the MPI Environment

    OpenAIRE

    2012-01-01

    Current genetic algorithm require both management of huge amounts of data and heavy computation, fulfilling these requirements calls for simple ways to implement parallel computing. In this paper, serial genetic algorithm was designed to parallel GA; this technology appears to be particularly well adapted to this task. Here we introduce two related mechanism: elite reserve strategy and MPI. The first can increase the possible to get the optimal solution of the population, while the message pa...

  14. A Hybrid Genetic Algorithm for the Job Shop Scheduling Problem

    OpenAIRE

    Gonçalves, José Fernando; Mendes, J. J. M.; Resende, Maurício G. C.

    2005-01-01

    This paper presents a hybrid genetic algorithm for the Job Shop Scheduling problem. The chromosome representation of the problem is based on random keys. The schedules are constructed using a priority rule in which the priorities are defined by the genetic algorithm. Schedules are constructed using a procedure that generates parameterized active schedules. After a schedule is obtained a local search heuristic is applied to improve the solution. The approach is tested on a set o...

  15. Classifying performance impairment in response to sleep loss using pattern recognition algorithms on single session testing.

    Science.gov (United States)

    St Hilaire, Melissa A; Sullivan, Jason P; Anderson, Clare; Cohen, Daniel A; Barger, Laura K; Lockley, Steven W; Klerman, Elizabeth B

    2013-01-01

    There is currently no "gold standard" marker of cognitive performance impairment resulting from sleep loss. We utilized pattern recognition algorithms to determine which features of data collected under controlled laboratory conditions could most reliably identify cognitive performance impairment in response to sleep loss using data from only one testing session, such as would occur in the "real world" or field conditions. A training set for testing the pattern recognition algorithms was developed using objective Psychomotor Vigilance Task (PVT) and subjective Karolinska Sleepiness Scale (KSS) data collected from laboratory studies during which subjects were sleep deprived for 26-52h. The algorithm was then tested in data from both laboratory and field experiments. The pattern recognition algorithm was able to identify performance impairment with a single testing session in individuals studied under laboratory conditions using PVT, KSS, length of time awake and time of day information with sensitivity and specificity as high as 82%. When this algorithm was tested on data collected under real-world conditions from individuals whose data were not in the training set, accuracy of predictions for individuals categorized with low performance impairment were as high as 98%. Predictions for medium and severe performance impairment were less accurate. We conclude that pattern recognition algorithms may be a promising method for identifying performance impairment in individuals using only current information about the individual's behavior. Single testing features (e.g., number of PVT lapses) with high correlation with performance impairment in the laboratory setting may not be the best indicators of performance impairment under real-world conditions. Pattern recognition algorithms should be further tested for their ability to be used in conjunction with other assessments of sleepiness in real-world conditions to quantify performance impairment in response to sleep loss.

  16. Cluster Based Hybrid Niche Mimetic and Genetic Algorithm for Text Document Categorization

    Directory of Open Access Journals (Sweden)

    A. K. Santra

    2011-09-01

    Full Text Available An efficient cluster based hybrid niche mimetic and genetic algorithm for text document categorization to improve the retrieval rate of relevant document fetching is addressed. The proposal minimizes the processing of structuring the document with better feature selection using hybrid algorithm. In addition restructuring of feature words to associated documents gets reduced, in turn increases document clustering rate. The performance of the proposed work is measured in terms of cluster objects accuracy, term weight, term frequency and inverse document frequency. Experimental results demonstrate that it achieves very good performance on both feature selection and text document categorization, compared to other classifier methods.

  17. Evolving neural networks with genetic algorithms to study the string landscape

    Science.gov (United States)

    Ruehle, Fabian

    2017-08-01

    We study possible applications of artificial neural networks to examine the string landscape. Since the field of application is rather versatile, we propose to dynamically evolve these networks via genetic algorithms. This means that we start from basic building blocks and combine them such that the neural network performs best for the application we are interested in. We study three areas in which neural networks can be applied: to classify models according to a fixed set of (physically) appealing features, to find a concrete realization for a computation for which the precise algorithm is known in principle but very tedious to actually implement, and to predict or approximate the outcome of some involved mathematical computation which performs too inefficient to apply it, e.g. in model scans within the string landscape. We present simple examples that arise in string phenomenology for all three types of problems and discuss how they can be addressed by evolving neural networks from genetic algorithms.

  18. Neural Network Control Optimization based on Improved Genetic Algorithm

    Directory of Open Access Journals (Sweden)

    Zhaoyin Zhang

    2013-08-01

    Full Text Available To clearly find the effect of factors in network classification, the classification process of PNN is analyzed in detail. The XOR problem is described by PNN and the elements in PNN are also studied. Through simulations and combined with genetic algorithm, a novel PNN supervised learning algorithm is proposed. This algorithm introduces the classification accuracy of training samples to the network parameter learning. It adopts genetic algorithm to train the PNN smoothing parameter and hidden centric vector. Then the effects of hidden neuron number, hidden centric vector and smoothing parameter in PNN are verified in the experiments. It is shown that this algorithm is superior to other PNN learning algorithms on classification effect.

  19. 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...

  20. 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 ...

  1. Multiple Query Evaluation Based on an Enhanced Genetic Algorithm.

    Science.gov (United States)

    Tamine, Lynda; Chrisment, Claude; Boughanem, Mohand

    2003-01-01

    Explains the use of genetic algorithms to combine results from multiple query evaluations to improve relevance in information retrieval. Discusses niching techniques, relevance feedback techniques, and evolution heuristics, and compares retrieval results obtained by both genetic multiple query evaluation and classical single query evaluation…

  2. 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....

  3. The Applications of Genetic Algorithms in Medicine

    OpenAIRE

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

    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 algo...

  4. 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 c......, 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....

  5. An evaluation of algorithms designed to classify the results from frequency doubling perimetry

    NARCIS (Netherlands)

    Muskens, RPHM; Heeg, GP; Jansonius, NM

    2004-01-01

    All previously published algorithms for the interpretation of frequency doubling perimetry test results were compared in full-threshold mode in a large group of glaucoma patients (n = 452) and normal subjects (n = 237). Areas under the receiver-operating characteristic (ROC) curve ranged from 0.86 t

  6. Genetic evidence and integration of various data sources for classifying uncertain variants into a single model.

    Science.gov (United States)

    Goldgar, David E; Easton, Douglas F; Byrnes, Graham B; Spurdle, Amanda B; Iversen, Edwin S; Greenblatt, Marc S

    2008-11-01

    Genetic testing often results in the finding of a variant whose clinical significance is unknown. A number of different approaches have been employed in the attempt to classify such variants. For some variants, case-control, segregation, family history, or other statistical studies can provide strong evidence of direct association with cancer risk. For most variants, other evidence is available that relates to properties of the protein or gene sequence. In this work we propose a Bayesian method for assessing the likelihood that a variant is pathogenic. We discuss the assessment of prior probability, and how to combine the various sources of data into a statistically valid integrated assessment with a posterior probability of pathogenicity. In particular, we propose the use of a two-component mixture model to integrate these various sources of data and to estimate the parameters related to sensitivity and specificity of specific kinds of evidence. Further, we discuss some of the issues involved in this process and the assumptions that underpin many of the methods used in the evaluation process.

  7. Optimal Path Planning for Mobile Robot Using Tailored Genetic Algorithm

    Directory of Open Access Journals (Sweden)

    Dong Xiao Xian

    2013-07-01

    Full Text Available During routine inspecting, mobile robot may be requested to visit multiple locations to execute special tasks occasionally. This study aims at optimal path planning for multiple goals visiting task based on tailored genetic algorithm. The proposed algorithm will generate an optimal path that has the least idle time, which is proven to be more effective on evaluating a path in our previous work. In proposed algorithm, customized chromosome representing a path and genetic operators including repair and cut are developed and implemented. Afterwards, simulations are carried out to verify the effectiveness and applicability. Finally, analysis of simulation results is conducted and future work is addressed.

  8. 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.

  9. Fuzzy Control of Chaotic System with Genetic Algorithm

    Institute of Scientific and Technical Information of China (English)

    FANG Jian-an; GUO Zhao-xia; SHAO Shi-huang

    2002-01-01

    A novel approach to control the unpredictable behavior of chaotic systems is presented. The control algorithm is based on fuzzy logic control technique combined with genetic algorithm. The use of fuzzy logic allows for the implementation of human "rule-of-thumb" approach to decision making by employing linguistic variables. An improved Genetic Algorithm (GA) is used to learn to optimally select the fuzzy membership functions of the linguistic labels in the condition portion of each rule,and to automatically generate fuzzy control actions under each condition. Simulation results show that such an approach for the control of chaotic systems is both effective and robust.

  10. A Survey of Association Rule Mining Using Genetic Algorithm

    Directory of Open Access Journals (Sweden)

    Anubha Sharma

    2012-08-01

    Full Text Available Data mining is the analysis step of the "Knowledge Discovery in Databases" process, or KDD. It is the process that results in the discovery of new patterns in large data sets. It utilizes methods at the intersection of artificial intelligence, machine learning, statistics, and database systems. The overall goal of the data mining process is to extract knowledge from an existing data set and transform it into a human-understandable structure. In data mining, association rule learning is a popular and well researched method for discovering interesting relations between variables in large databases. Association rules are usually required to satisfy a user-specified minimum support and a user-specified minimum confidence at the same time. Genetic algorithm (GA is a search heuristic that mimics the process of natural evolution. This heuristic is routinely used to generate useful solutions to optimization and search problems. Genetic algorithms belong to the larger class of evolutionary algorithms, which generate solutions to optimization problems using techniques inspired by natural evolution, such as inheritance, mutation, selection, and crossover. In previous, many researchers have proposed Genetic Algorithms for mining interesting association rules from quantitative data. In this paper we represent a survey of Association Rule Mining Using Genetic Algorithm. The techniques are categorized based upon different approaches. This paper provides the major advancement in the approaches for association rule mining using genetic algorithms.

  11. Parallel Genetic Algorithm Based on the MPI Environment

    Directory of Open Access Journals (Sweden)

    Wen-Juan Liu

    2012-11-01

    Full Text Available Current genetic algorithm require both management of huge amounts of data and heavy computation, fulfilling these requirements calls for simple ways to implement parallel computing. In this paper, serial genetic algorithm was designed to parallel GA; this technology appears to be particularly well adapted to this task. Here we introduce two related mechanism: elite reserve strategy and MPI. The first can increase the possible to get the optimal solution of the population, while the message passing interface MPI support is adding to form a new coarse-grain model of distributed parallel genetic algorithm. This new algorithm is tested by the classical and effective Knapsack problem, analysis shows that, the introduction of the parallel strategies can reduce the communication between different machines and the scheduling time of the heterogeneous system, thereby accelerate the traditional genetic algorithm search process, ultimately concluded that the parallel genetic algorithm is very promising and this framework could have a wide range of applications while maintaining good computational efficiency, scalability and ease of maintenance.

  12. Solving Classification Problems Using Genetic Programming Algorithms on GPUs

    Science.gov (United States)

    Cano, Alberto; Zafra, Amelia; Ventura, Sebastián

    Genetic Programming is very efficient in problem solving compared to other proposals but its performance is very slow when the size of the data increases. This paper proposes a model for multi-threaded Genetic Programming classification evaluation using a NVIDIA CUDA GPUs programming model to parallelize the evaluation phase and reduce computational time. Three different well-known Genetic Programming classification algorithms are evaluated using the parallel evaluation model proposed. Experimental results using UCI Machine Learning data sets compare the performance of the three classification algorithms in single and multithreaded Java, C and CUDA GPU code. Results show that our proposal is much more efficient.

  13. Coarse-Grained Parallel Genetic Algorithm to solve the Shortest Path Routing problem using Genetic operators

    Directory of Open Access Journals (Sweden)

    V.PURUSHOTHAM REDDY

    2011-02-01

    Full Text Available In computer networks the routing is based on shortest path routing algorithms. Based on its advantages, an alternative method is used known as Genetic Algorithm based routing algorithm, which is highly scalable and insensitive to variations in network topology. Here we propose a coarse-grained parallel genetic algorithm to solve the shortest path routing problem with the primary goal of computation time reduction along with the use of migration scheme. This algorithm is developed and implemented on an MPI cluster. The effects of migration and its performance is studied in this paper.

  14. Novel hybrid genetic algorithm for progressive multiple sequence alignment.

    Science.gov (United States)

    Afridi, Muhammad Ishaq

    2013-01-01

    The family of evolutionary or genetic algorithms is used in various fields of bioinformatics. Genetic algorithms (GAs) can be used for simultaneous comparison of a large pool of DNA or protein sequences. This article explains how the GA is used in combination with other methods like the progressive multiple sequence alignment strategy to get an optimal multiple sequence alignment (MSA). Optimal MSA get much importance in the field of bioinformatics and some other related disciplines. Evolutionary algorithms evolve and improve their performance. In this optimisation, the initial pair-wise alignment is achieved through a progressive method and then a good objective function is used to select and align more alignments and profiles. Child and subpopulation initialisation is based upon changes in the probability of similarity or the distance matrix of the alignment population. In this genetic algorithm, optimisation of mutation, crossover and migration in the population of candidate solution reflect events of natural organic evolution.

  15. Adaptive interactive genetic algorithms with individual interval fitness

    Institute of Scientific and Technical Information of China (English)

    Dunwei Gong; Guangsong Guo; Li Lu; Hongmei Ma

    2008-01-01

    It is necessary to enhance the performance of interactive genetic algorithms in order to apply them to complicated optimization problems successfully. An adaptive interactive genetic algorithm with individual interval fitness is proposed in this paper in which an individual fitness is expressed by an interval. Through analyzing the fitness, information reflecting the distribution of an evolutionary population is picked up, namely, the difference of evaluating superior individuals and the difference of evaluating a population. Based on these, the adaptive probabilities of crossover and mutation operators of an individual are presented. The algorithm proposed in this paper is applied to a fashion evolutionary design system, and the results show that it can find many satisfactory solutions per generation. The achievement of the paper provides a new approach to enhance the performance of interactive genetic algorithms.

  16. A Multi-Objective Genetic Algorithm for Optimal Portfolio Problems

    Institute of Scientific and Technical Information of China (English)

    林丹; 赵瑞

    2004-01-01

    This paper concerns with modeling and design of an algorithm for the portfolio selection problems with fixed transaction costs and minimum transaction lots. A mean-variance model for the portfolio selection problem is proposed, and the model is formulated as a non-smooth and nonlinear integer programming problem with multiple objective functions. As it has been proven that finding a feasible solution to the problem only is already NP-hard, based on NSGA-II and genetic algorithm for numerical optimization of constrained problems (Genocop), a multi-objective genetic algorithm (MOGA) is designed to solve the model. Its features comprise integer encoding and corresponding operators, and special treatment of constraints conditions. It is illustrated via a numerical example that the genetic algorithm can efficiently solve portfolio selection models proposed in this paper. This approach offers promise for the portfolio problems in practice.

  17. Optimization of a genetic algorithm for searching molecular conformer space

    Science.gov (United States)

    Brain, Zoe E.; Addicoat, Matthew A.

    2011-11-01

    We present two sets of tunings that are broadly applicable to conformer searches of isolated molecules using a genetic algorithm (GA). In order to find the most efficient tunings for the GA, a second GA - a meta-genetic algorithm - was used to tune the first genetic algorithm to reliably find the already known a priori correct answer with minimum computational resources. It is shown that these tunings are appropriate for a variety of molecules with different characteristics, and most importantly that the tunings are independent of the underlying model chemistry but that the tunings for rigid and relaxed surfaces differ slightly. It is shown that for the problem of molecular conformational search, the most efficient GA actually reduces to an evolutionary algorithm.

  18. Stochastic analysis and convergence velocity estimation of genetic algorithms

    Institute of Scientific and Technical Information of China (English)

    郭观七; 喻寿益

    2003-01-01

    Formulizations of mutation and crossover operators independent of representation of solutions are proposed. A kind of precisely quantitative Markov chain of populations of standard genetic algorithms is modeled. It is proved that inadequate parameters of mutation and crossover probabilities degenerate standard genetic algorithm to a class of random search algorithms without selection bias toward any solution based on fitness. After introducing elitist reservation, the stochastic matrix of Markov chain of the best-so-far individual with the highest fitness is derived.The average convergence velocity of genetic algorithms is defined as the mathematical expectation of the mean absorbing time steps that the best-so-far individual transfers from any initial solution to the global optimum. Using the stochastic matrix of the best-so-far individual, a theoretic method and the computing process of estimating the average convergence velocity are proposed.

  19. An Indirect Genetic Algorithm for a Nurse Scheduling Problem

    CERN Document Server

    Aickelin, Uwe

    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 these permutations. Computational experiments based on 52 weeks of live data are used to evaluate three different decoders with varying levels of intelligence, and four well-known crossover operators. Results are further enhanced by introducing a hybrid crossover operator and by making use of simple bounds to reduce the size of the solution space. The results reveal that the proposed algorithm is able to find high quality solutions and is both faster and more flexible than a recently published Tabu Search approach.

  20. Solving the MDBCS Problem Using the Metaheuric–Genetic Algorithm

    Directory of Open Access Journals (Sweden)

    Milena Bogdanovic

    2011-12-01

    Full Text Available The problems degree-limited graph of nodes considering the weight of the vertex or weight of the edges, with the aim to find the optimal weighted graph in terms of certain restrictions on the degree of the vertices in the subgraph. This class of combinatorial problems was extensively studied because of the implementation and application in network design, connection of networks and routing algorithms. It is likely that solution of MDBCS problem will find its place and application in these areas. The paper is given an ILP model to solve the problem MDBCS, as well as the genetic algorithm, which calculates a good enough solution for the input graph with a greater number of nodes. An important feature of the heuristic algorithms is that can approximate, but still good enough to solve the problems of exponential complexity. However, it should solve the problem heuristic algorithms may not lead to a satisfactory solution, and that for some of the problems, heuristic algorithms give relatively poor results. This is particularly true of problems for which no exact polynomial algorithm complexity. Also, heuristic algorithms are not the same, because some parts of heuristic algorithms differ depending on the situation and problems in which they are used. These parts are usually the objective function (transformation, and their definition significantly affects the efficiency of the algorithm. By mode of action, genetic algorithms are among the methods directed random search space solutions are looking for a global optimum.

  1. Composite multiobjective optimization beamforming based on genetic algorithms

    Institute of Scientific and Technical Information of China (English)

    Shi Jing; Meng Weixiao; Zhang Naitong; Wang Zheng

    2006-01-01

    All thc parameters of beamforming are usually optimized simultaneously in implementing the optimization of antenna array pattern with multiple objectives and parameters by genetic algorithms (GAs).Firstly, this paper analyzes the performance of fitness functions of previous algorithms. It shows that original algorithms make the fitness functions too complex leading to large amount of calculation, and also the selection of the weight of parameters very sensitive due to many parameters optimized simultaneously. This paper proposes a kind of algorithm of composite beamforming, which detaches the antenna array into two parts corresponding to optimization of different objective parameters respectively. New algorithm substitutes the previous complex fitness function with two simpler functions. Both theoretical analysis and simulation results show that this method simplifies the selection of weighting parameters and reduces the complexity of calculation. Furthermore, the algorithm has better performance in lowering side lobe and interferences in comparison with conventional algorithms of beamforming in the case of slightly widening the main lobe.

  2. Algorithms for Image Analysis and Combination of Pattern Classifiers with Application to Medical Diagnosis

    CERN Document Server

    Georgiou, Harris

    2009-01-01

    Medical Informatics and the application of modern signal processing in the assistance of the diagnostic process in medical imaging is one of the more recent and active research areas today. This thesis addresses a variety of issues related to the general problem of medical image analysis, specifically in mammography, and presents a series of algorithms and design approaches for all the intermediate levels of a modern system for computer-aided diagnosis (CAD). The diagnostic problem is analyzed with a systematic approach, first defining the imaging characteristics and features that are relevant to probable pathology in mammo-grams. Next, these features are quantified and fused into new, integrated radio-logical systems that exhibit embedded digital signal processing, in order to improve the final result and minimize the radiological dose for the patient. In a higher level, special algorithms are designed for detecting and encoding these clinically interest-ing imaging features, in order to be used as input to ...

  3. Solving traveling salesman problems by genetic algorithms

    Institute of Scientific and Technical Information of China (English)

    2003-01-01

    The gene section ordering on solving traveling salesman problems is analyzed by numerical experiments. Some improved crossover operations are presented. Several combinations of genetic operations are examined and the functions of these operations are analyzed. The essentiality of the ordering of the gene section and the significance of the evolutionary inversion operation are discussed. Some results and conclusions are obtained and given, which provide useful information for the implementation of the genetic operations for solving the traveling salesman problem.

  4. Examining applying high performance genetic data feature selection and classification algorithms for colon cancer diagnosis.

    Science.gov (United States)

    Al-Rajab, Murad; Lu, Joan; Xu, Qiang

    2017-07-01

    This paper examines the accuracy and efficiency (time complexity) of high performance genetic data feature selection and classification algorithms for colon cancer diagnosis. The need for this research derives from the urgent and increasing need for accurate and efficient algorithms. Colon cancer is a leading cause of death worldwide, hence it is vitally important for the cancer tissues to be expertly identified and classified in a rapid and timely manner, to assure both a fast detection of the disease and to expedite the drug discovery process. In this research, a three-phase approach was proposed and implemented: Phases One and Two examined the feature selection algorithms and classification algorithms employed separately, and Phase Three examined the performance of the combination of these. It was found from Phase One that the Particle Swarm Optimization (PSO) algorithm performed best with the colon dataset as a feature selection (29 genes selected) and from Phase Two that the Support Vector Machine (SVM) algorithm outperformed other classifications, with an accuracy of almost 86%. It was also found from Phase Three that the combined use of PSO and SVM surpassed other algorithms in accuracy and performance, and was faster in terms of time analysis (94%). It is concluded that applying feature selection algorithms prior to classification algorithms results in better accuracy than when the latter are applied alone. This conclusion is important and significant to industry and society. Copyright © 2017 Elsevier B.V. All rights reserved.

  5. A Hybrid Immigrants Scheme for Genetic Algorithms in Dynamic Environments

    Institute of Scientific and Technical Information of China (English)

    Shengxiang Yang; Renato Tinós

    2007-01-01

    Dynamic optimization problems are a kind of optimization problems that involve changes over time. They pose a serious challenge to traditional optimization methods as well as conventional genetic algorithms since the goal is no longer to search for the optimal solution(s) of a fixed problem but to track the moving optimum over time. Dynamic optimization problems have attracted a growing interest from the genetic algorithm community in recent years. Several approaches have been developed to enhance the performance of genetic algorithms in dynamic environments. One approach is to maintain the diversity of the population via random immigrants. This paper proposes a hybrid immigrants scheme that combines the concepts of elitism, dualism and random immigrants for genetic algorithms to address dynamic optimization problems. In this hybrid scheme, the best individual, i.e., the elite, from the previous generation and its dual individual are retrieved as the bases to create immigrants via traditional mutation scheme. These elitism-based and dualism-based immigrants together with some random immigrants are substituted into the current population, replacing the worst individuals in the population. These three kinds of immigrants aim to address environmental changes of slight, medium and significant degrees respectively and hence efficiently adapt genetic algorithms to dynamic environments that are subject to different severities of changes. Based on a series of systematically constructed dynamic test problems, experiments are carried out to investigate the performance of genetic algorithms with the hybrid immigrants scheme and traditional random immigrants scheme. Experimental results validate the efficiency of the proposed hybrid immigrants scheme for improving the performance of genetic algorithms in dynamic environments.

  6. Classifying Algorithm Based on a Fuzzy Neural network for the control of a Network Attached Optical Jukebox

    Institute of Scientific and Technical Information of China (English)

    LIU Xuan; JIA Hui-bo; CHENG Ming

    2006-01-01

    A new analytical method for improving the performance of a network attached optical jukebox is presented by means of artificial neural networks. Through analyzing operation (request) process in this system,the mathematics model and algorithm are built for this storage system,and then a classified method based on artificial neural networks for this system is proposed. Simulation results testified the feasibility and validity of the proposed method that it could overcome the drawbacks of the frequent I/O operation and provide an effective way for using the Network Attached Optical Jukebox.

  7. Multiscale Unsupervised Segmentation of SAR Imagery Using the Genetic Algorithm.

    Science.gov (United States)

    Wen, Xian-Bin; Zhang, Hua; Jiang, Ze-Tao

    2008-03-12

    A valid unsupervised and multiscale segmentation of synthetic aperture radar(SAR) imagery is proposed by a combination GA-EM of the Expectation Maximization(EM) algorith with the genetic algorithm (GA). The mixture multiscale autoregressive(MMAR) model is introduced to characterize and exploit the scale-to-scale statisticalvariations and statistical variations in the same scale in SAR imagery due to radar speckle,and a segmentation method is given by combining the GA algorithm with the EMalgorithm. This algorithm is capable of selecting the number of components of the modelusing the minimum description length (MDL) criterion. Our approach benefits from theproperties of the Genetic and the EM algorithm by combination of both into a singleprocedure. The population-based stochastic search of the genetic algorithm (GA) exploresthe search space more thoroughly than the EM method. Therefore, our algorithm enablesescaping from local optimal solutions since the algorithm becomes less sensitive to itsinitialization. Some experiment results are given based on our proposed approach, andcompared to that of the EM algorithms. The experiments on the SAR images show that theGA-EM outperforms the EM method.

  8. Multiscale Unsupervised Segmentation of SAR Imagery Using the Genetic Algorithm

    Directory of Open Access Journals (Sweden)

    Ze-Tao Jiang

    2008-03-01

    Full Text Available A valid unsupervised and multiscale segmentation of synthetic aperture radar(SAR imagery is proposed by a combination GA-EM of the Expectation Maximization(EM algorith with the genetic algorithm (GA. The mixture multiscale autoregressive(MMAR model is introduced to characterize and exploit the scale-to-scale statisticalvariations and statistical variations in the same scale in SAR imagery due to radar speckle,and a segmentation method is given by combining the GA algorithm with the EMalgorithm. This algorithm is capable of selecting the number of components of the modelusing the minimum description length (MDL criterion. Our approach benefits from theproperties of the Genetic and the EM algorithm by combination of both into a singleprocedure. The population-based stochastic search of the genetic algorithm (GA exploresthe search space more thoroughly than the EM method. Therefore, our algorithm enablesescaping from local optimal solutions since the algorithm becomes less sensitive to itsinitialization. Some experiment results are given based on our proposed approach, andcompared to that of the EM algorithms. The experiments on the SAR images show that theGA-EM outperforms the EM method.

  9. A Genetic Clustering Algorithm for Mean-Residual Vector Quantization

    Institute of Scientific and Technical Information of China (English)

    CHUShuchuan; JohnF.Roddick; CHENTsongyi

    2004-01-01

    Vector quantization (VQ) is a useful tool for data compression and can be applied to compress the data vectors in the database. The quality of the recovered data vector depends on a good codebook. Meanresidual vector quantization (M/R VQ) has been shown to be efficient in the encoding time and it only needs a little storage. In this paper, genetic algorithms in combination with the Generalized lloyd algorithm (GLA) are applied to the codebook design of M/R VQ. The mean codebook and residual codebook are trained using GLA algorithm separately, then Genetic algorithms (GA) are used to evaluate and evolve the combined mean codebook and residual codebook. The parameters used in the proposed algorithm are designed based on experiments and they are robust to the proposed GA based clustering algorithm for M/R VQ. Experimental results demonstrate the proposed genetic clustering algorithm applied to M/R VQ may improve the peak signal to noise ratio of the recovered data vector compared with the GLA algorithm.

  10. An Agent Inspired Reconfigurable Computing Implementation of a Genetic Algorithm

    Science.gov (United States)

    Weir, John M.; Wells, B. Earl

    2003-01-01

    Many software systems have been successfully implemented using an agent paradigm which employs a number of independent entities that communicate with one another to achieve a common goal. The distributed nature of such a paradigm makes it an excellent candidate for use in high speed reconfigurable computing hardware environments such as those present in modem FPGA's. In this paper, a distributed genetic algorithm that can be applied to the agent based reconfigurable hardware model is introduced. The effectiveness of this new algorithm is evaluated by comparing the quality of the solutions found by the new algorithm with those found by traditional genetic algorithms. The performance of a reconfigurable hardware implementation of the new algorithm on an FPGA is compared to traditional single processor implementations.

  11. Weighted K-Nearest Neighbor Classification Algorithm Based on Genetic Algorithm

    Directory of Open Access Journals (Sweden)

    Xuesong Yan

    2013-10-01

    Full Text Available K-Nearest Neighbor (KNN is one of the most popular algorithms for data classification. Many researchers have found that the KNN algorithm accomplishes very good performance in their experiments on different datasets. The traditional KNN text classification algorithm has limitations: calculation complexity, the performance is solely dependent on the training set, and so on. To overcome these limitations, an improved version of KNN is proposed in this paper, we use genetic algorithm combined with weighted KNN to improve its classification performance. and the experiment results shown that our proposed algorithm outperforms the KNN with greater accuracy.

  12. Quantum Algorithms and the Genetic Code

    CERN Document Server

    Patel, A D

    2001-01-01

    Replication of DNA and synthesis of proteins are studied from the view-pointof quantum database search. Identification of a base-pairing with a quantumquery gives a natural (and first ever!) explanation of why living organismshave 4 nucleotide bases and 20 amino acids. It is amazing that these numbersarise as solutions to an optimisation problem. Components of the DNA structurewhich implement Grover's algorithm are identified, and a physical scenario ispresented for the execution of the quantum algorithm. It is proposed thatenzymes play a crucial role in maintaining quantum coherence of the process.Experimental tests that can verify this scenario are pointed out.

  13. Solving constrained traveling salesman problems by genetic algorithms

    Institute of Scientific and Technical Information of China (English)

    WU Chunguo; LIANG Yanchun; LEE Heowpueh; LU Chun; LIN Wuzhong

    2004-01-01

    Three kinds of constrained traveling salesman problems (TSP) arising from application problems, namely the open route TSP, the end-fixed TSP, and the path-constrained TSP, are proposed. The corresponding approaches based on modified genetic algorithms (GA) for solving these constrained TSPs are presented. Numerical experiments demonstrate that the algorithm for the open route TSP shows its advantages when the open route is required, the algorithm for the end-fixed TSP can deal with route optimization with constraint of fixed ends effectively, and the algorithm for the path-constraint could benefit the traffic problems where some cities cannot be visited from each other.

  14. Linkage intensity learning approach with genetic algorithm for causality diagram

    Institute of Scientific and Technical Information of China (English)

    WANG Cheng-liang; CHEN Juan-juan

    2007-01-01

    The causality diagram theory, which adopts graphical expression of knowledge and direct intensity of causality, overcomes some shortages in belief network and has evolved into a mixed causality diagram methodology for discrete and continuous variable. But to give linkage intensity of causality diagram is difficult, particularly in many working conditions in which sampling data are limited or noisy. The classic learning algorithm is hard to be adopted. We used genetic algorithm to learn linkage intensity from limited data. The simulation results demonstrate that this algorithm is more suitable than the classic algorithm in the condition of sample shortage such as space shuttle's fault diagnoisis.

  15. Multi-Objective Feature Subset Selection using Non-dominated Sorting Genetic Algorithm

    Directory of Open Access Journals (Sweden)

    A. Khan

    2015-02-01

    Full Text Available This paper presents an evolutionary algorithm based technique to solve multi-objective feature subset selection problem. The data used for classification contains large number of features called attributes. Some of these attributes are not relevant and needs to be eliminated. In classification procedure, each feature has an effect on the accuracy, cost and learning time of the classifier. So, there is a strong requirement to select a subset of the features before building the classifier. This proposed technique treats feature subset selection as multi-objective optimization problem. This research uses one of the latest multi-objective genetic algorithms (NSGA - II. The fitness value of a particular feature subset is measured by using ID3. The testing accuracy acquired is then assigned to the fitness value. This technique is tested on several datasets taken from the UCI machine repository. The experiments demonstrate the feasibility of using NSGA-II for feature subset selection.

  16. Integrating genetic algorithm method with neural network for land use classification using SZ-3 CMODIS data

    Institute of Scientific and Technical Information of China (English)

    WANG Changyao; LUO Chengfeng; LIU Zhengjun

    2005-01-01

    This paper presents a methodology on land use mapping using CMODIS (Chinese Moderate Resolution Imaging Spectroradiometer ) data on-board SZ-3 (Shenzhou 3) spacecraft. The integrated method is composed of genetic algorithm (GA) for feature extraction and neural network classifier for land use classification. In the data preprocessing, a moment matching method was adopted to reuse classification was obtained. To generate a land use map, the three layers back propagation neural network classifier is used for training the samples and classification. Compared with the Maximum Likelihood classification algorithm, the results show that the accuracy of land use classification is obviously improved by using our proposed method, the selected band number in the classification process is reduced,and the computational performance for training and classification is improved. The result also shows that the CMODIS data can be effectively used for land use/land cover classification and change monitoring at regional and global scale.

  17. A fuzzy hill-climbing algorithm for the development of a compact associative classifier

    Science.gov (United States)

    Mitra, Soumyaroop; Lam, Sarah S.

    2012-02-01

    Classification, a data mining technique, has widespread applications including medical diagnosis, targeted marketing, and others. Knowledge discovery from databases in the form of association rules is one of the important data mining tasks. An integrated approach, classification based on association rules, has drawn the attention of the data mining community over the last decade. While attention has been mainly focused on increasing classifier accuracies, not much efforts have been devoted towards building interpretable and less complex models. This paper discusses the development of a compact associative classification model using a hill-climbing approach and fuzzy sets. The proposed methodology builds the rule-base by selecting rules which contribute towards increasing training accuracy, thus balancing classification accuracy with the number of classification association rules. The results indicated that the proposed associative classification model can achieve competitive accuracies on benchmark datasets with continuous attributes and lend better interpretability, when compared with other rule-based systems.

  18. Discharges Classification using Genetic Algorithms and Feature Selection Algorithms on Time and Frequency Domain Data Extracted from Leakage Current Measurements

    Directory of Open Access Journals (Sweden)

    D. Pylarinos

    2013-12-01

    Full Text Available 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. time domain features, 3. frequency domain features, 4. t-test selected features 5. mRMR selected features. Results are discussed and compared with previous classification implementations on this particular data group.

  19. AN ADAPTIVE REGION GROWING ALGORITHM WITH SUPPORT VECTOR MACHINE CLASSIFIER FOR TUBERCULOSIS CAVITY IDENTIFICATION

    Directory of Open Access Journals (Sweden)

    K. Meenakshi Sundaram

    2013-01-01

    Full Text Available The major world health menace is Tuberculosis (TB that has the effect on one-third of the global population and millions of new infections are occurring every year. The cavities in the upper lung zone are a strong indicator that the disease has developed into a highly contagious state. The study includes 52 Chest X-ray images with Tuberculosis and 43 Chest X-ray images without Tuberculosis. The identification of the TB cavities is mostly conducted by the clinicians by observing the chest radiographs. But the automatic screening has lot of advantages such as substantial reduction in the labor workload of clinicians, enhancing the sensitivity of the test and better precision in diagnosis by increasing the number of images that can be analyzed by the computer. Many researchers have proposed different techniques to improve the performance of automatic screening process. This paper improves the accuracy over the existing technique using the adaptive region growing property and SVM classifier. Initially, pre-processing is carried out for the input image using Gaussian filtering technique to make the image suitable for further processing. The contours of the image will be obtained using region growing technique. The SVM classifier is then used to confirm the suspected TB cavities. The classification will be carried out by the features which the study has taken from the segmented image. The proposed technique is implemented in MATLAB and the performance is compared with the existing technique. From the result, the study has achieved eighty-five percentage accuracy over the existing technique’s seventy eight-percentage accuracy.

  20. Genetic Algorithms, Neural Networks, and Time Effectiveness Algorithm Based Air Combat Intelligence Simulation System

    Institute of Scientific and Technical Information of China (English)

    曾宪钊; 成冀; 安欣; 方礼明

    2002-01-01

    This paper introduces a new Air Combat Intelligence Simulation System (ACISS) in a 32 versus 32 air combat, describes three methods: Genetic Algorithms (GA) in the multi-targeting decision and Evading Missile Rule Base learning, Neural Networks (NN) in the maneuvering decision, and Time Effectiveness Algorithm (TEA) in the adjudicating an air combat and the evaluating evading missile effectiveness.

  1. Application of Modified Genetic Algorithm to Optimal Design of Supporting Structure

    Institute of Scientific and Technical Information of China (English)

    ZHOU Rui-zhong; PAN Shi-wei

    2003-01-01

    The modified genetic algorithm was used for the optimal design of supporting structure in deep pits.Based on the common genetic algorithm, using niche technique and reserving the optimum individual the modified genetic algorithm was presented. By means of the practical engineering, the modified genetic algorithm not only has more expedient convergence, but also can enhance security and operation efficiency.

  2. 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.

  3. 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.

  4. Characterization of mammographic masses using a gradient-based segmentation algorithm and a neural classifier

    CERN Document Server

    Delogu, P; Kasae, P; Retico, A

    2008-01-01

    The computer-aided diagnosis system we developed for the mass characterization is mainly based on a segmentation algorithm and on the neural classification of several features computed on the segmented mass. Mass segmentation plays a key role in most computerized systems. Our technique is a gradient-based one, showing the main characteristic that no free parameters have been evaluated on the dataset used in this analysis, thus it can directly be applied to datasets acquired in different conditions without any ad-hoc modification. A dataset of 226 masses (109 malignant and 117 benign) has been used in this study. The segmentation algorithm works with a comparable efficiency both on malignant and benign masses. Sixteen features based on shape, size and intensity of the segmented masses are analyzed by a multi-layered perceptron neural network. A feature selection procedure has been carried out on the basis of the feature discriminating power and of the linear correlations interplaying among them. The comparison...

  5. Impatient Task Mapping in Elastic Cloud using Genetic Algorithm

    Directory of Open Access Journals (Sweden)

    Nawfal A. Mehdi

    2011-01-01

    Full Text Available Problem statement: Task scheduling is the main factor that determines the performance of any distributed system. Cloud computing comes with a paradigm of distributed datacenters. Each datacenter consists of physical machines that host virtual machines to execute customers’ tasks. Resources allocation on the cloud is different from other paradigms and the mapping algorithms need to be adapted to the new characteristics. This study takes the problem of immediate task scheduling under an intercloud infrastructure using a genetic algorithm. An impatient task needs to be scheduled as soon as it enters the system taking into account the input and output files location and its QoS requirements. Approach: This study proposes an algorithm that can find a fast mapping using genetic algorithms with "exist if satisfy" condition to speed up the mapping process and ensures the respecting of all task deadlines. Cloudsim simulator was used to test the proposed algorithm with real datasets collected as a cloud benchmark. Mapping time and makespan are the performance metrics that are used to evaluate the proposed system. Results: The results show an improvement in the proposed system compared to MCT algorithm as illustrated throughout the study. Conclusion: Batch mapping via genetic algorithms with throughput as a fitness function can be used to map jobs to cloud resources.

  6. 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.

  7. Quantum algorithms and the genetic code

    Indian Academy of Sciences (India)

    Apoorva Patel

    2001-02-01

    Replication of DNA and synthesis of proteins are studied from the view-point of quantum database search. Identification of a base-pairing with a quantum query gives a natural (and first ever!) explanation of why living organisms have 4 nucleotide bases and 20 amino acids. It is amazing that these numbers arise as solutions to an optimisation problem. Components of the DNA structure which implement Grover’s algorithm are identified, and a physical scenario is presented for the execution of the quantum algorithm. It is proposed that enzymes play a crucial role in maintaining quantum coherence of the process. Experimental tests that can verify this scenario are pointed out.

  8. Emotion recognition using eigenvalues and Levenberg–Marquardt algorithm-based classifier

    Indian Academy of Sciences (India)

    VILAS H GAIDHANE; YOGESH V HOTE; VIJANDER SINGH

    2016-04-01

    In this paper, a simple and computationally efficient approach is proposed for person independent facial emotion recognition. The proposed approach is based on the significant features of an image, i.e., the collection of few largest eigenvalues (LE). Further, a Levenberg–Marquardt algorithm-based neural network (LMNN) is applied for multiclass emotions classification. This leads to a new facial emotion recognition approach (LE-LMNN) which is systematically examined on JAFFE and Cohn–Kanade databases. Experimental results illustrate that the LE-LMNN approach is effective and computationally efficient for facial emotion recognition. The robustness of the proposed approach is also tested on low-resolution facial emotion images.The performance of the proposed approach is found to be superior as compared to the various existing methods.

  9. Memory based active contour algorithm using pixel-level classified images for colon crypt segmentation.

    Science.gov (United States)

    Cohen, Assaf; Rivlin, Ehud; Shimshoni, Ilan; Sabo, Edmond

    2015-07-01

    In this paper, we introduce a novel method for detection and segmentation of crypts in colon biopsies. Most of the approaches proposed in the literature try to segment the crypts using only the biopsy image without understanding the meaning of each pixel. The proposed method differs in that we segment the crypts using an automatically generated pixel-level classification image of the original biopsy image and handle the artifacts due to the sectioning process and variance in color, shape and size of the crypts. The biopsy image pixels are classified to nuclei, immune system, lumen, cytoplasm, stroma and goblet cells. The crypts are then segmented using a novel active contour approach, where the external force is determined by the semantics of each pixel and the model of the crypt. The active contour is applied for every lumen candidate detected using the pixel-level classification. Finally, a false positive crypt elimination process is performed to remove segmentation errors. This is done by measuring their adherence to the crypt model using the pixel level classification results. The method was tested on 54 biopsy images containing 4944 healthy and 2236 cancerous crypts, resulting in 87% detection of the crypts with 9% of false positive segments (segments that do not represent a crypt). The segmentation accuracy of the true positive segments is 96%.

  10. 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.

  11. Using an Adaptive Genetic Algorithm to Improve Finance Decision

    Institute of Scientific and Technical Information of China (English)

    FaliangGui; TiesongHu

    2004-01-01

    Optimising both qualitative and quantitative factors is a key challenge in solving construction finance decisions. The semi-structured nature of construction finance optimisation problems precludes conventional optimisation techniques. With a desire to improve the performance of the canonical genetic algorithm (CCA) which is characterised by static crossover and mutation probability, and to provide contractors with a profit-risk trade-off curve and cash flow prediction, an adaptive genetic algorithm (AGA) model is developed. Ten projects being undertaken by a major construction firm in Hong Kong were used as case studies to evaluate the performance of the genetic algorithm (CA). The results of case study reveal that the ACA outperformed the CGA both in terms of its quality of solutions and the computational time required for a certain level of accuracy. The results also indicate that there is a potential for using the GA for modelling financial decisions should both quantitative and qualitative factors be optimised simultaneously.

  12. Cheating for Problem Solving: A Genetic Algorithm with Social Interactions

    CERN Document Server

    Lahoz-Beltra, Rafeal; Aickelin, Uwe

    2010-01-01

    We propose a variation of the standard genetic algorithm that incorporates social interaction between the individuals in the population. Our goal is to understand the evolutionary role of social systems and its possible application as a non-genetic new step in evolutionary algorithms. In biological populations, ie animals, even human beings and microorganisms, social interactions often affect the fitness of individuals. It is conceivable that the perturbation of the fitness via social interactions is an evolutionary strategy to avoid trapping into local optimum, thus avoiding a fast convergence of the population. We model the social interactions according to Game Theory. The population is, therefore, composed by cooperator and defector individuals whose interactions produce payoffs according to well known game models (prisoner's dilemma, chicken game, and others). Our results on Knapsack problems show, for some game models, a significant performance improvement as compared to a standard genetic algorithm.

  13. Application of genetic algorithms to hydrogenated silicon clusters

    Indian Academy of Sciences (India)

    N Chakraborti; R Prasad

    2003-01-01

    We discuss the application of biologically inspired genetic algorithms to determine the ground state structures of a number of Si–H clusters. The total energy of a given configuration of a cluster has been obtained by using a non-orthogonal tight-binding model and the energy minimization has been carried out by using genetic algorithms and their recent variant differential evolution. Our results for ground state structures and cohesive energies for Si–H clusters are in good agreement with the earlier work conducted using the simulated annealing technique. We find that the results obtained by genetic algorithms turn out to be comparable and often better than the results obtained by the simulated annealing technique.

  14. Method of stereo matching based on genetic algorithm

    Science.gov (United States)

    Lu, Chaohui; An, Ping; Zhang, Zhaoyang

    2003-09-01

    A new stereo matching scheme based on image edge and genetic algorithm (GA) is presented to improve the conventional stereo matching method in this paper. In order to extract robust edge feature for stereo matching, infinite symmetric exponential filter (ISEF) is firstly applied to remove the noise of image, and nonlinear Laplace operator together with local variance of intensity are then used to detect edges. Apart from the detected edge, the polarity of edge pixels is also obtained. As an efficient search method, genetic algorithm is applied to find the best matching pair. For this purpose, some new ideas are developed for applying genetic algorithm to stereo matching. Experimental results show that the proposed methods are effective and can obtain good results.

  15. Family Competition Pheromone Genetic Algorithm for Comparative Genome Assembly

    Institute of Scientific and Technical Information of China (English)

    Chien-Hao Su; Chien-Shun Chiou; Jung-Che Kuo; Pei-Jen Wang; Cheng-Yan Kao; Hsueh-Ting Chu

    2014-01-01

    Genome assembly is a prerequisite step for analyzing next generation sequencing data and also far from being solved. Many assembly tools have been proposed and used extensively. Majority of them aim to assemble sequencing reads into contigs; however, we focus on the assembly of contigs into scaffolds in this paper. This is called scaffolding, which estimates the relative order of the contigs as well as the size of the gaps between these contigs. Pheromone trail-based genetic algorithm (PGA) was previously proposed and had decent performance according to their paper. From our previous study, we found that family competition mechanism in genetic algorithm is able to further improve the results. Therefore, we propose family competition pheromone genetic algorithm (FCPGA) and demonstrate the improvement over PGA.

  16. 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.

  17. 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.

  18. Multi-island Genetic Algorithm Opetimization of Suspension System

    Directory of Open Access Journals (Sweden)

    Li-Wei Xu

    2012-11-01

    Full Text Available The suspension and the car's operating stability are closely linked. Through the optimization of the suspension, it can improve the operating stability of vehicle, which is very meaningful to enhance the performance of modern cars. With the development of science and technology, the traditional optimization methods often appear insufficient when it deals with the multi-objective optimization problem of the automotive suspension. As a kind of improved genetic algorithm, the multi-island genetic algorithm can handle the multi-objective problem very well. In order to improve the vehicle handling stability, in this paper, the multi-island genetic algorithm is used to optimize the suspension parameters, combined with the iSight-FD and the Adams/car.

  19. 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.

  20. 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.

  1. A Comparison Study of Classifier Algorithms for Cross-Person Physical Activity Recognition.

    Science.gov (United States)

    Saez, Yago; Baldominos, Alejandro; Isasi, Pedro

    2016-12-30

    Physical activity is widely known to be one of the key elements of a healthy life. The many benefits of physical activity described in the medical literature include weight loss and reductions in the risk factors for chronic diseases. With the recent advances in wearable devices, such as smartwatches or physical activity wristbands, motion tracking sensors are becoming pervasive, which has led to an impressive growth in the amount of physical activity data available and an increasing interest in recognizing which specific activity a user is performing. Moreover, big data and machine learning are now cross-fertilizing each other in an approach called "deep learning", which consists of massive artificial neural networks able to detect complicated patterns from enormous amounts of input data to learn classification models. This work compares various state-of-the-art classification techniques for automatic cross-person activity recognition under different scenarios that vary widely in how much information is available for analysis. We have incorporated deep learning by using Google's TensorFlow framework. The data used in this study were acquired from PAMAP2 (Physical Activity Monitoring in the Ageing Population), a publicly available dataset containing physical activity data. To perform cross-person prediction, we used the leave-one-subject-out (LOSO) cross-validation technique. When working with large training sets, the best classifiers obtain very high average accuracies (e.g., 96% using extra randomized trees). However, when the data volume is drastically reduced (where available data are only 0.001% of the continuous data), deep neural networks performed the best, achieving 60% in overall prediction accuracy. We found that even when working with only approximately 22.67% of the full dataset, we can statistically obtain the same results as when working with the full dataset. This finding enables the design of more energy-efficient devices and facilitates cold

  2. A Comparison Study of Classifier Algorithms for Cross-Person Physical Activity Recognition

    Directory of Open Access Journals (Sweden)

    Yago Saez

    2016-12-01

    Full Text Available Physical activity is widely known to be one of the key elements of a healthy life. The many benefits of physical activity described in the medical literature include weight loss and reductions in the risk factors for chronic diseases. With the recent advances in wearable devices, such as smartwatches or physical activity wristbands, motion tracking sensors are becoming pervasive, which has led to an impressive growth in the amount of physical activity data available and an increasing interest in recognizing which specific activity a user is performing. Moreover, big data and machine learning are now cross-fertilizing each other in an approach called “deep learning”, which consists of massive artificial neural networks able to detect complicated patterns from enormous amounts of input data to learn classification models. This work compares various state-of-the-art classification techniques for automatic cross-person activity recognition under different scenarios that vary widely in how much information is available for analysis. We have incorporated deep learning by using Google’s TensorFlow framework. The data used in this study were acquired from PAMAP2 (Physical Activity Monitoring in the Ageing Population, a publicly available dataset containing physical activity data. To perform cross-person prediction, we used the leave-one-subject-out (LOSO cross-validation technique. When working with large training sets, the best classifiers obtain very high average accuracies (e.g., 96% using extra randomized trees. However, when the data volume is drastically reduced (where available data are only 0.001% of the continuous data, deep neural networks performed the best, achieving 60% in overall prediction accuracy. We found that even when working with only approximately 22.67% of the full dataset, we can statistically obtain the same results as when working with the full dataset. This finding enables the design of more energy-efficient devices and

  3. Classification of traumatic brain injury severity using informed data reduction in a series of binary classifier algorithms.

    Science.gov (United States)

    Prichep, Leslie S; Jacquin, Arnaud; Filipenko, Julie; Dastidar, Samanwoy Ghosh; Zabele, Stephen; Vodencarević, Asmir; Rothman, Neil S

    2012-11-01

    Assessment of medical disorders is often aided by objective diagnostic tests which can lead to early intervention and appropriate treatment. In the case of brain dysfunction caused by head injury, there is an urgent need for quantitative evaluation methods to aid in acute triage of those subjects who have sustained traumatic brain injury (TBI). Current clinical tools to detect mild TBI (mTBI/concussion) are limited to subjective reports of symptoms and short neurocognitive batteries, offering little objective evidence for clinical decisions; or computed tomography (CT) scans, with radiation-risk, that are most often negative in mTBI. This paper describes a novel methodology for the development of algorithms to provide multi-class classification in a substantial population of brain injured subjects, across a broad age range and representative subpopulations. The method is based on age-regressed quantitative features (linear and nonlinear) extracted from brain electrical activity recorded from a limited montage of scalp electrodes. These features are used as input to a unique "informed data reduction" method, maximizing confidence of prospective validation and minimizing over-fitting. A training set for supervised learning was used, including: "normal control," "concussed," and "structural injury/CT positive (CT+)." The classifier function separating CT+ from the other groups demonstrated a sensitivity of 96% and specificity of 78%; the classifier separating "normal controls" from the other groups demonstrated a sensitivity of 81% and specificity of 74%, suggesting high utility of such classifiers in acute clinical settings. The use of a sequence of classifiers where the desired risk can be stratified further supports clinical utility.

  4. Constrained minimization of smooth functions using a genetic algorithm

    Science.gov (United States)

    Moerder, Daniel D.; Pamadi, Bandu N.

    1994-01-01

    The use of genetic algorithms for minimization of differentiable functions that are subject to differentiable constraints is considered. A technique is demonstrated for converting the solution of the necessary conditions for a constrained minimum into an unconstrained function minimization. This technique is extended as a global constrained optimization algorithm. The theory is applied to calculating minimum-fuel ascent control settings for an energy state model of an aerospace plane.

  5. Control of Complex Systems Using Bayesian Networks and Genetic Algorithm

    CERN Document Server

    Marwala, Tshilidzi

    2007-01-01

    A method based on Bayesian neural networks and genetic algorithm is proposed to control the fermentation process. The relationship between input and output variables is modelled using Bayesian neural network that is trained using hybrid Monte Carlo method. A feedback loop based on genetic algorithm is used to change input variables so that the output variables are as close to the desired target as possible without the loss of confidence level on the prediction that the neural network gives. The proposed procedure is found to reduce the distance between the desired target and measured outputs significantly.

  6. 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.

  7. Optimization of multicast optical networks with genetic algorithm

    Science.gov (United States)

    Lv, Bo; Mao, Xiangqiao; Zhang, Feng; Qin, Xi; Lu, Dan; Chen, Ming; Chen, Yong; Cao, Jihong; Jian, Shuisheng

    2007-11-01

    In this letter, aiming to obtain the best multicast performance of optical network in which the video conference information is carried by specified wavelength, we extend the solutions of matrix games with the network coding theory and devise a new method to solve the complex problems of multicast network switching. In addition, an experimental optical network has been testified with best switching strategies by employing the novel numerical solution designed with an effective way of genetic algorithm. The result shows that optimal solutions with genetic algorithm are accordance with the ones with the traditional fictitious play method.

  8. A genetic algorithm for structure-activity relationships: software implementation

    CERN Document Server

    Jantschi, Lorentz

    2009-01-01

    The design and the implementation of a genetic algorithm are described. The applicability domain is on structure-activity relationships expressed as multiple linear regressions and predictor variables are from families of structure-based molecular descriptors. An experiment to compare different selection and survival strategies was designed and realized. The genetic algorithm was run using the designed experiment on a set of 206 polychlorinated biphenyls searching on structure-activity relationships having known the measured octanol-water partition coefficients and a family of molecular descriptors. The experiment shows that different selection and survival strategies create different partitions on the entire population of all possible genotypes.

  9. 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.

  10. Optimal design of steel portal frames based on genetic algorithms

    Institute of Scientific and Technical Information of China (English)

    Yue CHEN; Kai HU

    2008-01-01

    As for the optimal design of steel portal frames, due to both the complexity of cross selections of beams and columns and the discreteness of design variables, it is difficult to obtain satisfactory results by traditional optimization. Based on a set of constraints of the Technical Specification for Light-weighted Steel Portal Frames of China, a genetic algorithm (GA) optimization program for portal frames, written in MATLAB code, was proposed in this paper. The graph user interface (GUI) is also developed for this optimal program, so that it can be used much more conveniently. Finally, some examples illustrate the effectiveness and efficiency of the genetic-algorithm-based optimal program.

  11. Building Blocks Propagation in Quantum-Inspired Genetic Algorithm

    CERN Document Server

    Nowotniak, Robert

    2010-01-01

    This paper presents an analysis of building blocks propagation in Quantum-Inspired Genetic Algorithm, which belongs to a new class of metaheuristics drawing their inspiration from both biological evolution and unitary evolution of quantum systems. The expected number of quantum chromosomes matching a schema has been analyzed and a random variable corresponding to this issue has been introduced. The results have been compared with Simple Genetic Algorithm. Also, it has been presented how selected binary quantum chromosomes cover a domain of one-dimensional fitness function.

  12. 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.

  13. Solving the Vehicle Routing Problem using Genetic Algorithm

    Directory of Open Access Journals (Sweden)

    Abdul Kadar Muhammad Masum

    2011-08-01

    Full Text Available The main goal of this research is to find a solution of Vehicle Routing Problem using genetic algorithms. The Vehicle Routing Problem (VRP is a complex combinatorial optimization problem that belongs to the NP-complete class. Due to the nature of the problem it is not possible to use exact methods for large instances of the VRP. Genetic algorithms provide a search technique used in computing to find true or approximate solution to optimization and search problems. However we used some heuristic in addition during crossover or mutation for tuning the system to obtain better result.

  14. Stellar Population Analysis of Galaxies based on Genetic Algorithms

    Institute of Scientific and Technical Information of China (English)

    Abdel-Fattah Attia; H.A.Ismail; I.M.Selim; A.M.Osman; I.A.Isaa; M.A.Marie; A.A.Shaker

    2005-01-01

    We present a new method for determining the age and relative contribution of different stellar populations in galaxies based on the genetic algorithm.We apply this method to the barred spiral galaxy NGC 3384, using CCD images in U, B, V, R and I bands. This analysis indicates that the galaxy NGC 3384 is mainly inhabited by old stellar population (age > 109 yr). Some problems were encountered when numerical simulations are used for determining the contribution of different stellar populations in the integrated color of a galaxy. The results show that the proposed genetic algorithm can search efficiently through the very large space of the possible ages.

  15. Genetic Algorithm Optimisation of a Ship Navigation System

    Directory of Open Access Journals (Sweden)

    E. Alfaro-Cid

    2001-01-01

    Full Text Available The optimisation of the PID controllers' gains for separate propulsion and heading control systems of CyberShip I, a scale model of an oil platform supply ship, using Genetic Algorithms is considered. During the initial design process both PID controllers have been manually tuned to improve their performance. However this tuning approach is a tedious and time consuming process. A solution to this problem is the use of optimisation techniques based on Genetic Algorithms to optimise the controllers' gain values. This investigation has been carried out through computer-generated simulations based on a non-linear hydrodynamic model of CyberShip I.

  16. Quality of Service Routing Strategy Using Supervised Genetic Algorithm

    Institute of Scientific and Technical Information of China (English)

    WANG Zhaoxia; SUN Yugeng; WANG Zhiyong; SHEN Huayu

    2007-01-01

    A supervised genetic algorithm (SGA) is proposed to solve the quality of service (QoS)routing problems in computer networks. The supervised rules of intelligent concept are introduced into genetic algorithms (GAs) to solve the constraint optimization problem. One of the main characteristics of SGA is its searching space can be limited in feasible regions rather than infeasible regions. The superiority of SGA to other GAs lies in that some supervised search rules in which the information comes from the problems are incorporated into SGA. The simulation results show that SGA improves the ability of searching an optimum solution and accelerates the convergent process up to 20 times.

  17. Genetic Algorithm Modeling with GPU Parallel Computing Technology

    CERN Document Server

    Cavuoti, Stefano; Brescia, Massimo; Pescapé, Antonio; Longo, Giuseppe; Ventre, Giorgio

    2012-01-01

    We present a multi-purpose genetic algorithm, designed and implemented with GPGPU / CUDA parallel computing technology. The model was derived from a multi-core CPU serial implementation, named GAME, already scientifically successfully tested and validated on astrophysical massive data classification problems, through a web application resource (DAMEWARE), specialized in data mining based on Machine Learning paradigms. Since genetic algorithms are inherently parallel, the GPGPU computing paradigm has provided an exploit of the internal training features of the model, permitting a strong optimization in terms of processing performances and scalability.

  18. Optimizing Combination of Units Commitment Based on Improved Genetic Algorithms

    Institute of Scientific and Technical Information of China (English)

    LAI Yifei; ZHANG Qianhua; JIA Junping

    2007-01-01

    GAs are general purpose optimization techniques based on principles inspired from the biological evolution using metaphors of mechanisms, such as natural selection, genetic recombination and survival of the fittest. By use of coding betterment, the dynamic changes of the mutation rate and the crossover probability, the dynamic choice of subsistence, the reservation of the optimal fitness value, a modified genetic algorithm for optimizing combination of units in thermal power plants is proposed.And through taking examples, test result are analyzed and compared with results of some different algorithms. Numerical results show available value for the unit commitment problem with examples.

  19. Hierarchical Genetic Algorithm Approach to Determine Pulse Sequences in NMR

    CERN Document Server

    Ajoy, Ashok

    2009-01-01

    We develop a new class of genetic algorithm that computationally determines efficient pulse sequences to implement a quantum gate U in a three-qubit system. The method is shown to be quite general, and the same algorithm can be used to derive efficient sequences for a variety of target matrices. We demonstrate this by implementing the inversion-on-equality gate efficiently when the spin-spin coupling constants $J_{12}=J_{23}=J$ and $J_{13}=0$. We also propose new pulse sequences to implement the Parity gate and Fanout gate, which are about 50% more efficient than the previous best efforts. Moreover, these sequences are shown to require significantly less RF power for their implementation. The proposed algorithm introduces several new features in the conventional genetic algorithm framework. We use matrices instead of linear chains, and the columns of these matrices have a well defined hierarchy. The algorithm is a genetic algorithm coupled to a fast local optimizer, and is hence a hybrid GA. It shows fast con...

  20. Multi Population Hybrid Genetic Algorithms for University Course Timetabling Problem

    Directory of Open Access Journals (Sweden)

    Leila Jadidi

    2012-06-01

    Full Text Available University course timetabling is one of the important and time consuming issues that each University is involved with it at the beginning of each. 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 near optimal feasible solutions instead of exact solutions. Genetic algorithms, because of multidirectional search property of them, 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 proposed algorithms, fuzzy logic is used to measure violation of soft constraints in fitness function to deal with inherent uncertainly 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.

  1. 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

  2. Genetic Algorithms and Their Application to the Protein Folding Problem

    Science.gov (United States)

    1993-12-01

    mutation, genetic algorithms simulate the Darwin theory of survival of the fittest. The search space is represented by a population of strings upon which... Darwin theory of survival of the fittest by representing the search space as a population of strings upon which genetic operators act to create new...34 International Conference on Tools for Artificial Intelligence, IEEE-TAI 90, 322-7. Cartwright , H. M. & Mott, G. F. (1991). "Looking A:;und: Using Clues

  3. Underground water quality model inversion of genetic algorithm

    Institute of Scientific and Technical Information of China (English)

    MA Ruijie; LI Xin

    2009-01-01

    The underground water quality model with non-linear inversion problem is ill-posed, and boils down to solving the minimum of nonlinear function. Genetic algorithms are adopted in a number of individuals of groups by iterative search to find the optimal solution of the problem, the encoding strings as its operational objective, and achieving the iterative calculations by the genetic operators. It is an effective method of inverse problems of groundwater, with incomparable advantages and practical significances.

  4. A new automatic alignment technology for single mode fiber-waveguide based on improved genetic algorithm

    Institute of Scientific and Technical Information of China (English)

    ZHENG Yu; CHEN Zhuang-zhuang; LI Ya-juan; DUAN Jian

    2009-01-01

    A novel automatic alignment algorithm of single mode fiber-waveguide based on improved genetic algorithm is proposed. The genetic searching is based on the dynamic crossover operator and the adaptive mutation operator to solve the premature convergence of simple genetic algorithm The improved genetic algorithm combines with hill-climbing method and pattern searching algorithm, to solve low precision of simple genetic algorithm in later searching. The simulation results indicate that the improved genetic algorithm can rise the alignment precision and reach the coupling loss of 0.01 dB when platform moves near 207 space points averagely.

  5. Manipulator Neural Network Control Based on Fuzzy Genetic Algorithm

    Institute of Scientific and Technical Information of China (English)

    2001-01-01

    The three-layer forward neural networks are used to establish the inverse kinem a tics models of robot manipulators. The fuzzy genetic algorithm based on the line ar scaling of the fitness value is presented to update the weights of neural net works. To increase the search speed of the algorithm, the crossover probability and the mutation probability are adjusted through fuzzy control and the fitness is modified by the linear scaling method in FGA. Simulations show that the propo sed method improves considerably the precision of the inverse kinematics solutio ns for robot manipulators and guarantees a rapid global convergence and overcome s the drawbacks of SGA and the BP algorithm.

  6. 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...

  7. 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...

  8. 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.

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

    DEFF Research Database (Denmark)

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

    2007-01-01

    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....

  10. Haplotyping a single triploid individual based on genetic algorithm.

    Science.gov (United States)

    Wu, Jingli; Chen, Xixi; Li, Xianchen

    2014-01-01

    The minimum error correction model is an important combinatorial model for haplotyping a single individual. In this article, triploid individual haplotype reconstruction problem is studied by using the model. A genetic algorithm based method GTIHR is presented for reconstructing the triploid individual haplotype. A novel coding method and an effectual hill-climbing operator are introduced for the GTIHR algorithm. This relatively short chromosome code can lead to a smaller solution space, which plays a positive role in speeding up the convergence process. The hill-climbing operator ensures algorithm GTIHR converge at a good solution quickly, and prevents premature convergence simultaneously. The experimental results prove that algorithm GTIHR can be implemented efficiently, and can get higher reconstruction rate than previous algorithms.

  11. Advancing x-ray scattering metrology using inverse genetic algorithms

    Science.gov (United States)

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

    2016-07-01

    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.

  12. 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.

  13. 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.

  14. FPGA Implementation of Parallel Particle Swarm Optimization Algorithm and Compared with Genetic Algorithm

    Directory of Open Access Journals (Sweden)

    BEN AMEUR Mohamed sadek

    2016-08-01

    Full Text Available In this paper, a digital implementation of Particle Swarm Optimization algorithm (PSO is developed for implementation on Field Programmable Gate Array (FPGA. PSO is a recent intelligent heuristic search method in which the mechanism of algorithm is inspired by the swarming of biological populations. PSO is similar to the Genetic Algorithm (GA. In fact, both of them use a combination of deterministic and probabilistic rules. The experimental results of this algorithm are effective to evaluate the performance of the PSO compared to GA and other PSO algorithm. New digital solutions are available to generate a hardware implementation of PSO Algorithms. Thus, we developed a hardware architecture based on Finite state machine (FSM and implemented into FPGA to solve some dispatch computing problems over other circuits based on swarm intelligence. Moreover, the inherent parallelism of these new hardware solutions with a large computational capacity makes the running time negligible regardless the complexity of the processing.

  15. 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.

  16. 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.

  17. Feature Reduction Based on Genetic Algorithm and Hybrid Model for Opinion Mining

    Directory of Open Access Journals (Sweden)

    P. Kalaivani

    2015-01-01

    Full Text Available With the rapid growth of websites and web form the number of product reviews is available on the sites. An opinion mining system is needed to help the people to evaluate emotions, opinions, attitude, and behavior of others, which is used to make decisions based on the user preference. In this paper, we proposed an optimized feature reduction that incorporates an ensemble method of machine learning approaches that uses information gain and genetic algorithm as feature reduction techniques. We conducted comparative study experiments on multidomain review dataset and movie review dataset in opinion mining. The effectiveness of single classifiers Naïve Bayes, logistic regression, support vector machine, and ensemble technique for opinion mining are compared on five datasets. The proposed hybrid method is evaluated and experimental results using information gain and genetic algorithm with ensemble technique perform better in terms of various measures for multidomain review and movie reviews. Classification algorithms are evaluated using McNemar’s test to compare the level of significance of the classifiers.

  18. Comparison of feature and classifier algorithms for online automatic sleep staging based on a single EEG signal.

    Science.gov (United States)

    Radha, Mustafa; Garcia-Molina, Gary; Poel, Mannes; Tononi, Giulio

    2014-01-01

    Automatic sleep staging on an online basis has recently emerged as a research topic motivated by fundamental sleep research. The aim of this paper is to find optimal signal processing methods and machine learning algorithms to achieve online sleep staging on the basis of a single EEG signal. The classification performance obtained using six different EEG signals and various signal processing feature sets is compared using the kappa statistic which has very recently become popular in sleep staging research. A variable duration of the EEG segment (or epoch) to decide on the sleep stage is also analyzed. Spectral-domain, time-domain, linear, and nonlinear features are compared in terms of performance and two types of machine learning approaches (random forests and support vector machines) are assessed. We have determined that frontal EEG signals, with spectral linear features, epoch durations between 18 and 30 seconds, and a random forest classifier lead to optimal classification performance while ensuring real-time online operation.

  19. Genetic algorithm for multi-protocol label switching

    Institute of Scientific and Technical Information of China (English)

    2007-01-01

    A new method for multi-protocol label switching is presented in this study, whose core idea is to construct model for simulating process of accommodating network online loads and then adopt genetic algorithm to optimize the model. Due to the heuristic property of evolutional method, the new method is efficient and effective, which is verified by the experiments.

  20. Optimization of reliability allocation strategies through use of genetic algorithms

    Energy Technology Data Exchange (ETDEWEB)

    Campbell, J.E.; Painton, L.A.

    1996-08-01

    This paper examines a novel optimization technique called genetic algorithms and its application to the optimization of reliability allocation strategies. Reliability allocation should occur in the initial stages of design, when the objective is to determine an optimal breakdown or allocation of reliability to certain components or subassemblies in order to meet system specifications. The reliability allocation optimization is applied to the design of a cluster tool, a highly complex piece of equipment used in semiconductor manufacturing. The problem formulation is presented, including decision variables, performance measures and constraints, and genetic algorithm parameters. Piecewise ``effort curves`` specifying the amount of effort required to achieve a certain level of reliability for each component of subassembly are defined. The genetic algorithm evolves or picks those combinations of ``effort`` or reliability levels for each component which optimize the objective of maximizing Mean Time Between Failures while staying within a budget. The results show that the genetic algorithm is very efficient at finding a set of robust solutions. A time history of the optimization is presented, along with histograms or the solution space fitness, MTBF, and cost for comparative purposes.

  1. 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.

  2. 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...

  3. 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 m

  4. Experiences with the PGAPack Parallel Genetic Algorithm library

    Energy Technology Data Exchange (ETDEWEB)

    Levine, D.; Hallstrom, P.; Noelle, D.; Walenz, B.

    1997-07-01

    PGAPack is the first widely distributed parallel genetic algorithm library. Since its release, several thousand copies have been distributed worldwide to interested users. In this paper we discuss the key components of the PGAPack design philosophy and present a number of application examples that use PGAPack.

  5. UAV Cooperative Multiple Task Assignments using Genetic Algorithms

    Science.gov (United States)

    2005-06-01

    vehicle routing problem (VRP). In all of these classical problems the minimum cost assignment is sought where: in the TSP the tour is of one agent...10] Baker, Barrie, M. and Ayechew, M. A., “A Genetic Algorithm for the Vehicle Routing Problem ,” Computers and Operations Research, Vol. 30, 2003

  6. 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 m

  7. Waypoint planning with Dubins Curves using Genetic Algorithms

    DEFF Research Database (Denmark)

    Hansen, Karl Damkjær; La Cour-Harbo, Anders

    2016-01-01

    , 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...

  8. 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 ...

  9. 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)

  10. Optimization of antibacterial peptides by genetic algorithms and cheminformatics

    DEFF Research Database (Denmark)

    Fjell, Christopher D.; Jenssen, Håvard; Cheung, Warren A.

    2011-01-01

    47 of the top rated 50 peptides chosen from an in silico library of nearly 100 000 sequences. Here, we report a method of generating candidate peptide sequences using the heuristic evolutionary programming method of genetic algorithms (GA), which provided a large (19-fold) improvement...

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

    DEFF Research Database (Denmark)

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

    2013-01-01

    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...

  12. 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...

  13. 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...

  14. 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.

  15. 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...

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

    Science.gov (United States)

    2015-03-26

    the mutation and crossover functions specified that certain design parameters be integer values [17]. Equation 21 represents the variables that...been used to force certain design variables to be integer values. Understanding the MATLAB code for the mutation and crossover functions is not...NAVIGATION CONSTELLATION DESIGN USING A MULTI-OBJECTIVE GENETIC ALGORITHM THESIS MARCH 2015

  17. 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...

  18. Optimization of composite panels using neural networks and genetic algorithms

    NARCIS (Netherlands)

    Ruijter, W.; Spallino, R.; Warnet, Laurent; de Boer, Andries

    2003-01-01

    The objective of this paper is to present first results of a running study on optimization of aircraft components (composite panels of a typical vertical tail plane) by using Genetic Algorithms (GA) and Neural Networks (NN). The panels considered are standardized to some extent but still there is a

  19. 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

  20. 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)

  1. 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 ...

  2. Finite-time performance analysis for genetic algorithms

    Institute of Scientific and Technical Information of China (English)

    2002-01-01

    Finite-time performance of genetic algorithm with elitist operator in finite solution space is studied, and the relationship between evolution generation and the quality of the solution found best so far is analyzed. The estimating formulations of the expectation value as well as upper bound and lower bound for the evolution generation earliest achieving specific performance are provided.

  3. Dimensional Synthesis of Four Bar Mechanism Using Genetic Algorithm

    Directory of Open Access Journals (Sweden)

    S. S. Shete

    2015-03-01

    Full Text Available The dimensional synthesis is done by using Genetic Algorithm to achieve a desired trajectory. Three problems are analyzed having different curvature. The program is authored in MATLAB® 2010a. The error is seen to be in the permissible prescribed limit. The prototyping of straight line trajectory analysis is also done in ADAMS®

  4. Transmission function models of finite population genetic algorithms

    NARCIS (Netherlands)

    Kemenade, C.H.M. van; Kok, J.N.; La Poutré, J.A.; Thierens, D.

    1998-01-01

    Infinite population models show a deterministic behaviour. Genetic algorithms with finite populations behave non-deterministicly. For small population sizes, the results obtained with these models differ strongly from the results predicted by the infinite population model. When the population size i

  5. 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

  6. A Parallel Genetic Algorithm for Automated Electronic Circuit Design

    Science.gov (United States)

    Lohn, Jason D.; Colombano, Silvano P.; Haith, Gary L.; Stassinopoulos, Dimitris; Norvig, Peter (Technical Monitor)

    2000-01-01

    We describe a parallel genetic algorithm (GA) that automatically generates circuit designs using evolutionary search. A circuit-construction programming language is introduced and we show how evolution can generate practical analog circuit designs. Our system allows circuit size (number of devices), circuit topology, and device values to be evolved. We present experimental results as applied to analog filter and amplifier design tasks.

  7. Genetic Algorithm Optimization for Determining Fuzzy Measures from Fuzzy Data

    Directory of Open Access Journals (Sweden)

    Chen Li

    2013-01-01

    Full Text Available Fuzzy measures and fuzzy integrals have been successfully used in many real applications. How to determine fuzzy measures is a very difficult problem in these applications. Though there have existed some methodologies for solving this problem, such as genetic algorithms, gradient descent algorithms, neural networks, and particle swarm algorithm, it is hard to say which one is more appropriate and more feasible. Each method has its advantages. Most of the existed works can only deal with the data consisting of classic numbers which may arise limitations in practical applications. It is not reasonable to assume that all data are real data before we elicit them from practical data. Sometimes, fuzzy data may exist, such as in pharmacological, financial and sociological applications. Thus, we make an attempt to determine a more generalized type of general fuzzy measures from fuzzy data by means of genetic algorithms and Choquet integrals. In this paper, we make the first effort to define the σ-λ rules. Furthermore we define and characterize the Choquet integrals of interval-valued functions and fuzzy-number-valued functions based on σ-λ rules. In addition, we design a special genetic algorithm to determine a type of general fuzzy measures from fuzzy data.

  8. 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.

  9. Diagnosis of Hepatocellular Carcinoma Spectroscopy Based on the Feature Selection Approach of the Genetic Algorithm

    Directory of Open Access Journals (Sweden)

    Shao-qing Wang

    2013-06-01

    Full Text Available This paper aims to study the application of medical imaging technology with artificial intelligence technology on how to improve the diagnostic accuracy rate for hepatocellular carcinoma. The   recognition method based on genetic algorithm (GA and Neural Network are presented. GA was used to select 20 optimal features from the 401 initial features. BP (Back-propagation Neural Network, BP and PNN (Probabilistic Neural Network, PNN were used to classify tested samples based on these optimized features, and make comparison between results based on 20 optimal features and the all 401 features. The results of the experiment show that the method can improve the recognition rate.

  10. Application of support vector machine and quantum genetic algorithm in infrared target recognition

    Science.gov (United States)

    Wang, Hongliang; Huang, Yangwen; Ding, Haifei

    2010-08-01

    In this paper, a kind of classifier based on support vector machine (SVM) is designed for infrared target recognition. In allusion to the problem how to choose kernel parameter and error penalty factor, quantum genetic algorithm (QGA) is used to optimize the parameters of SVM model, it overcomes the shortcoming of determining its parameters after trial and error in the past. Classification experiments of infrared target features extracted by this method show that the convergence speed is fast and the rate of accurate recognition is high.

  11. Critical Comparison of Multi-objective Optimization Methods: Genetic Algorithms versus Swarm Intelligence

    Directory of Open Access Journals (Sweden)

    V. Sedenka

    2010-09-01

    Full Text Available The paper deals with efficiency comparison of two global evolutionary optimization methods implemented in MATLAB. Attention is turned to an elitist Non-dominated Sorting Genetic Algorithm (NSGA-II and a novel multi-objective Particle Swarm Optimization (PSO. The performance of optimizers is compared on three different test functions and on a cavity resonator synthesis. The microwave resonator is modeled using the Finite Element Method (FEM. The hit rate and the quality of the Pareto front distribution are classified.

  12. Genetic algorithm-based evaluation of spatial straightness error

    Institute of Scientific and Technical Information of China (English)

    崔长彩; 车仁生; 黄庆成; 叶东; 陈刚

    2003-01-01

    A genetic algorithm ( GA ) -based approach is proposed to evaluate the straightness error of spatial lines. According to the mathematical definition of spatial straightness, a verification model is established for straightness error, and the fitness function of GA is then given and the implementation techniques of the proposed algorithm is discussed in detail. The implementation techniques include real number encoding, adaptive variable range choosing, roulette wheel and elitist combination selection strategies, heuristic crossover and single point mutation schemes etc. An application example is quoted to validate the proposed algorithm. The computation result shows that the GA-based approach is a superior nonlinear parallel optimization method. The performance of the evolution population can be improved through genetic operations such as reproduction, crossover and mutation until the optimum goal of the minimum zone solution is obtained. The quality of the solution is better and the efficiency of computation is higher than other methods.

  13. An Adaptive Immune Genetic Algorithm for Edge Detection

    Science.gov (United States)

    Li, Ying; Bai, Bendu; Zhang, Yanning

    An adaptive immune genetic algorithm (AIGA) based on cost minimization technique method for edge detection is proposed. The proposed AIGA recommends the use of adaptive probabilities of crossover, mutation and immune operation, and a geometric annealing schedule in immune operator to realize the twin goals of maintaining diversity in the population and sustaining the fast convergence rate in solving the complex problems such as edge detection. Furthermore, AIGA can effectively exploit some prior knowledge and information of the local edge structure in the edge image to make vaccines, which results in much better local search ability of AIGA than that of the canonical genetic algorithm. Experimental results on gray-scale images show the proposed algorithm perform well in terms of quality of the final edge image, rate of convergence and robustness to noise.

  14. A dynamic fuzzy clustering method based on genetic algorithm

    Institute of Scientific and Technical Information of China (English)

    ZHENG Yan; ZHOU Chunguang; LIANG Yanchun; GUO Dongwei

    2003-01-01

    A dynamic fuzzy clustering method is presented based on the genetic algorithm. By calculating the fuzzy dissimilarity between samples the essential associations among samples are modeled factually. The fuzzy dissimilarity between two samples is mapped into their Euclidean distance, that is, the high dimensional samples are mapped into the two-dimensional plane. The mapping is optimized globally by the genetic algorithm, which adjusts the coordinates of each sample, and thus the Euclidean distance, to approximate to the fuzzy dissimilarity between samples gradually. A key advantage of the proposed method is that the clustering is independent of the space distribution of input samples, which improves the flexibility and visualization. This method possesses characteristics of a faster convergence rate and more exact clustering than some typical clustering algorithms. Simulated experiments show the feasibility and availability of the proposed method.

  15. Genetic Algorithm Based Production Planning for Alternative Process Production

    Institute of Scientific and Technical Information of China (English)

    ZHANG Fa-ping; SUN Hou-fang; SHAHID I. Butt

    2009-01-01

    Production planning under flexible job shop environment is studied. A mathematic model is formulated to help improve alternative process production. This model, in which genetic algorithm is used, is expected to result in better production planning, hence towards the aim of minimizing production cost under the constraints of delivery time and other scheduling conditions. By means of this algorithm, all planning schemes which could meet all requirements of the constraints within the whole solution space are exhaustively searched so as to find the optimal one. Also, a case study is given in the end to support and validate this model. Our results show that genetic algorithm is capable of locating feasible process routes to reduce production cost for certain tasks.

  16. Genetic algorithms for multicriteria shape optimization of induction furnace

    Science.gov (United States)

    Kůs, Pavel; Mach, František; Karban, Pavel; Doležel, Ivo

    2012-09-01

    In this contribution we deal with a multi-criteria shape optimization of an induction furnace. We want to find shape parameters of the furnace in such a way, that two different criteria are optimized. Since they cannot be optimized simultaneously, instead of one optimum we find set of partially optimal designs, so called Pareto front. We compare two different approaches to the optimization, one using nonlinear conjugate gradient method and second using variation of genetic algorithm. As can be seen from the numerical results, genetic algorithm seems to be the right choice for this problem. Solution of direct problem (coupled problem consisting of magnetic and heat field) is done using our own code Agros2D. It uses finite elements of higher order leading to fast and accurate solution of relatively complicated coupled problem. It also provides advanced scripting support, allowing us to prepare parametric model of the furnace and simply incorporate various types of optimization algorithms.

  17. Efficient Satellite Scheduling Based on Improved Vector Evaluated Genetic Algorithm

    Directory of Open Access Journals (Sweden)

    Tengyue Mao

    2012-03-01

    Full Text Available Satellite scheduling is a typical multi-peak, many-valley, nonlinear multi-objective optimization problem. How to effectively implement the satellite scheduling is a crucial research in space areas.This paper mainly discusses the performance of VEGA (Vector Evaluated Genetic Algorithm based on the study of basic principles of VEGA algorithm, algorithm realization and test function, and then improves VEGA algorithm through introducing vector coding, new crossover and mutation operators, new methods to assign fitness and hold good individuals. As a result, the diversity and convergence of improved VEGA algorithm of improved VEGA algorithm have been significantly enhanced and will be applied to Earth-Mars orbit optimization. At the same time, this paper analyzes the results of the improved VEGA, whose results of performance analysis and evaluation show that although VEGA has a profound impact upon multi-objective evolutionary research,  multi-objective evolutionary algorithm on the basis of Pareto seems to be a more effective method to get the non-dominated solutions from the perspective of diversity and convergence of experimental result. Finally, based on Visual C + + integrated development environment, we have implemented improved vector evaluation algorithm in the satellite scheduling.

  18. Application of genetic algorithm to hexagon-based motion estimation.

    Science.gov (United States)

    Kung, Chih-Ming; Cheng, Wan-Shu; Jeng, Jyh-Horng

    2014-01-01

    With the improvement of science and technology, the development of the network, and the exploitation of the HDTV, the demands of audio and video become more and more important. Depending on the video coding technology would be the solution for achieving these requirements. Motion estimation, which removes the redundancy in video frames, plays an important role in the video coding. Therefore, many experts devote themselves to the issues. The existing fast algorithms rely on the assumption that the matching error decreases monotonically as the searched point moves closer to the global optimum. However, genetic algorithm is not fundamentally limited to this restriction. The character would help the proposed scheme to search the mean square error closer to the algorithm of full search than those fast algorithms. The aim of this paper is to propose a new technique which focuses on combing the hexagon-based search algorithm, which is faster than diamond search, and genetic algorithm. Experiments are performed to demonstrate the encoding speed and accuracy of hexagon-based search pattern method and proposed method.

  19. Evaluation of algorithms used to order markers on genetic maps.

    Science.gov (United States)

    Mollinari, M; Margarido, G R A; Vencovsky, R; Garcia, A A F

    2009-12-01

    When building genetic maps, it is necessary to choose from several marker ordering algorithms and criteria, and the choice is not always simple. In this study, we evaluate the efficiency of algorithms try (TRY), seriation (SER), rapid chain delineation (RCD), recombination counting and ordering (RECORD) and unidirectional growth (UG), as well as the criteria PARF (product of adjacent recombination fractions), SARF (sum of adjacent recombination fractions), SALOD (sum of adjacent LOD scores) and LHMC (likelihood through hidden Markov chains), used with the RIPPLE algorithm for error verification, in the construction of genetic linkage maps. A linkage map of a hypothetical diploid and monoecious plant species was simulated containing one linkage group and 21 markers with fixed distance of 3 cM between them. In all, 700 F(2) populations were randomly simulated with 100 and 400 individuals with different combinations of dominant and co-dominant markers, as well as 10 and 20% of missing data. The simulations showed that, in the presence of co-dominant markers only, any combination of algorithm and criteria may be used, even for a reduced population size. In the case of a smaller proportion of dominant markers, any of the algorithms and criteria (except SALOD) investigated may be used. In the presence of high proportions of dominant markers and smaller samples (around 100), the probability of repulsion linkage increases between them and, in this case, use of the algorithms TRY and SER associated to RIPPLE with criterion LHMC would provide better results.

  20. JavaGenes and Condor: Cycle-Scavenging Genetic Algorithms

    Science.gov (United States)

    Globus, Al; Langhirt, Eric; Livny, Miron; Ramamurthy, Ravishankar; Soloman, Marvin; Traugott, Steve

    2000-01-01

    A genetic algorithm code, JavaGenes, was written in Java and used to evolve pharmaceutical drug molecules and digital circuits. JavaGenes was run under the Condor cycle-scavenging batch system managing 100-170 desktop SGI workstations. Genetic algorithms mimic biological evolution by evolving solutions to problems using crossover and mutation. While most genetic algorithms evolve strings or trees, JavaGenes evolves graphs representing (currently) molecules and circuits. Java was chosen as the implementation language because the genetic algorithm requires random splitting and recombining of graphs, a complex data structure manipulation with ample opportunities for memory leaks, loose pointers, out-of-bound indices, and other hard to find bugs. Java garbage-collection memory management, lack of pointer arithmetic, and array-bounds index checking prevents these bugs from occurring, substantially reducing development time. While a run-time performance penalty must be paid, the only unacceptable performance we encountered was using standard Java serialization to checkpoint and restart the code. This was fixed by a two-day implementation of custom checkpointing. JavaGenes is minimally integrated with Condor; in other words, JavaGenes must do its own checkpointing and I/O redirection. A prototype Java-aware version of Condor was developed using standard Java serialization for checkpointing. For the prototype to be useful, standard Java serialization must be significantly optimized. JavaGenes is approximately 8700 lines of code and a few thousand JavaGenes jobs have been run. Most jobs ran for a few days. Results include proof that genetic algorithms can evolve directed and undirected graphs, development of a novel crossover operator for graphs, a paper in the journal Nanotechnology, and another paper in preparation.

  1. A Linkage Learning Genetic Algorithm with Linkage Matrix

    Institute of Scientific and Technical Information of China (English)

    2006-01-01

    The goal of linkage learning, or building block identification, is the creation of a more effective Genetic Algorithm (GA). This paper proposes a new Linkage Learning Genetic Algorithms, named m-LLGA. With the linkage learning module and the linkage-based genetic operation, m-LLGA is not only able to learn and record the linkage information among genes without any prior knowledge of the function being optimized. It also can use the linkage information stored in the linkage matrix to guide the selection of crossover point. The preliminary experiments on two kinds of bounded difficulty problems and a TSP problem validated the performance of m-LLGA. The m-LLGA learns the linkage of different building blocks parallel and therefore solves these problems effectively; it can also reasonably reduce the probability of building blocks being disrupted by crossover at the same time give attention to getting away from local minimum.

  2. A simple algorithm to estimate genetic variance in an animal threshold model using Bayesian inference

    Directory of Open Access Journals (Sweden)

    Heringstad Bjørg

    2010-07-01

    Full Text Available Abstract Background In the genetic analysis of binary traits with one observation per animal, animal threshold models frequently give biased heritability estimates. In some cases, this problem can be circumvented by fitting sire- or sire-dam models. However, these models are not appropriate in cases where individual records exist on parents. Therefore, the aim of our study was to develop a new Gibbs sampling algorithm for a proper estimation of genetic (covariance components within an animal threshold model framework. Methods In the proposed algorithm, individuals are classified as either "informative" or "non-informative" with respect to genetic (covariance components. The "non-informative" individuals are characterized by their Mendelian sampling deviations (deviance from the mid-parent mean being completely confounded with a single residual on the underlying liability scale. For threshold models, residual variance on the underlying scale is not identifiable. Hence, variance of fully confounded Mendelian sampling deviations cannot be identified either, but can be inferred from the between-family variation. In the new algorithm, breeding values are sampled as in a standard animal model using the full relationship matrix, but genetic (covariance components are inferred from the sampled breeding values and relationships between "informative" individuals (usually parents only. The latter is analogous to a sire-dam model (in cases with no individual records on the parents. Results When applied to simulated data sets, the standard animal threshold model failed to produce useful results since samples of genetic variance always drifted towards infinity, while the new algorithm produced proper parameter estimates essentially identical to the results from a sire-dam model (given the fact that no individual records exist for the parents. Furthermore, the new algorithm showed much faster Markov chain mixing properties for genetic parameters (similar to

  3. Multi-user cognitive radio network resource allocation based on the adaptive niche immune genetic algorithm

    Institute of Scientific and Technical Information of China (English)

    Zu Yun-Xiao; Zhou Jie

    2012-01-01

    Multi-user cognitive radio network resource allocation based on the adaptive niche immune genetic algorithm is proposed,and a fitness function is provided.Simulations are conducted using the adaptive niche immune genetic algorithm,the simulated annealing algorithm,the quantum genetic algorithm and the simple genetic algorithm,respectively.The results show that the adaptive niche immune genetic algorithm performs better than the other three algorithms in terms of the multi-user cognitive radio network resource allocation,and has quick convergence speed and strong global searching capability,which effectively reduces the system power consumption and bit error rate.

  4. Multi-user cognitive radio network resource allocation based on the adaptive niche immune genetic algorithm

    Science.gov (United States)

    Zu, Yun-Xiao; Zhou, Jie

    2012-01-01

    Multi-user cognitive radio network resource allocation based on the adaptive niche immune genetic algorithm is proposed, and a fitness function is provided. Simulations are conducted using the adaptive niche immune genetic algorithm, the simulated annealing algorithm, the quantum genetic algorithm and the simple genetic algorithm, respectively. The results show that the adaptive niche immune genetic algorithm performs better than the other three algorithms in terms of the multi-user cognitive radio network resource allocation, and has quick convergence speed and strong global searching capability, which effectively reduces the system power consumption and bit error rate.

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

    Directory of Open Access Journals (Sweden)

    Zakir Hussain Ahmed

    2014-01-01

    Full Text Available 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.

  6. 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.

  7. 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.

  8. 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.)

  9. A novel algorithm combining finite state method and genetic algorithm for solving crude oil scheduling problem.

    Science.gov (United States)

    Duan, Qian-Qian; Yang, Gen-Ke; Pan, Chang-Chun

    2014-01-01

    A hybrid optimization algorithm combining finite state method (FSM) and genetic algorithm (GA) is proposed to solve the crude oil scheduling problem. The FSM and GA are combined to take the advantage of each method and compensate deficiencies of individual methods. In the proposed algorithm, the finite state method makes up for the weakness of GA which is poor at local searching ability. The heuristic returned by the FSM can guide the GA algorithm towards good solutions. The idea behind this is that we can generate promising substructure or partial solution by using FSM. Furthermore, the FSM can guarantee that the entire solution space is uniformly covered. Therefore, the combination of the two algorithms has better global performance than the existing GA or FSM which is operated individually. Finally, a real-life crude oil scheduling problem from the literature is used for conducting simulation. The experimental results validate that the proposed method outperforms the state-of-art GA method.

  10. 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.

  11. Strain gage selection in loads equations using a genetic algorithm

    Science.gov (United States)

    1994-01-01

    Traditionally, structural loads are measured using strain gages. A loads calibration test must be done before loads can be accurately measured. In one measurement method, a series of point loads is applied to the structure, and loads equations are derived via the least squares curve fitting algorithm using the strain gage responses to the applied point loads. However, many research structures are highly instrumented with strain gages, and the number and selection of gages used in a loads equation can be problematic. This paper presents an improved technique using a genetic algorithm to choose the strain gages used in the loads equations. Also presented are a comparison of the genetic algorithm performance with the current T-value technique and a variant known as the Best Step-down technique. Examples are shown using aerospace vehicle wings of high and low aspect ratio. In addition, a significant limitation in the current methods is revealed. The genetic algorithm arrived at a comparable or superior set of gages with significantly less human effort, and could be applied in instances when the current methods could not.

  12. A Clustering Genetic Algorithm for Cylinder Drag Optimization

    Science.gov (United States)

    Milano, Michele; Koumoutsakos, Petros

    2002-01-01

    A real coded genetic algorithm is implemented for the optimization of actuator parameters for cylinder drag minimization. We consider two types of idealized actuators that are allowed either to move steadily and tangentially to the cylinder surface (“belts”) or to steadily blow/suck with a zero net mass constraint. The genetic algorithm we implement has the property of identifying minima basins, rather than single optimum points. The knowledge of the shape of the minimum basin enables further insights into the system properties and provides a sensitivity analysis in a fully automated way. The drag minimization problem is formulated as an optimal regulation problem. By means of the clustering property of the present genetic algorithm, a set of solutions producing drag reduction of up to 50% is identified. A comparison between the two types of actuators, based on the clustering property of the algorithm, indicates that blowing/suction actuation parameters are associated with larger tolerances when compared to optimal parameters for the belt actuators. The possibility of using a few strategically placed actuators to obtain a significant drag reduction is explored using the clustering diagnostics of this method. The optimal belt-actuator parameters obtained by optimizing the two-dimensional case is employed in three-dimensional simulations, by extending the actuators across the span of the cylinder surface. The three-dimensional controlled flow exhibits a strong two-dimensional character near the cylinder surface, resulting in significant drag reduction.

  13. A novel pipeline based FPGA implementation of a genetic algorithm

    Science.gov (United States)

    Thirer, Nonel

    2014-05-01

    To solve problems when an analytical solution is not available, more and more bio-inspired computation techniques have been applied in the last years. Thus, an efficient algorithm is the Genetic Algorithm (GA), which imitates the biological evolution process, finding the solution by the mechanism of "natural selection", where the strong has higher chances to survive. A genetic algorithm is an iterative procedure which operates on a population of individuals called "chromosomes" or "possible solutions" (usually represented by a binary code). GA performs several processes with the population individuals to produce a new population, like in the biological evolution. To provide a high speed solution, pipelined based FPGA hardware implementations are used, with a nstages pipeline for a n-phases genetic algorithm. The FPGA pipeline implementations are constraints by the different execution time of each stage and by the FPGA chip resources. To minimize these difficulties, we propose a bio-inspired technique to modify the crossover step by using non identical twins. Thus two of the chosen chromosomes (parents) will build up two new chromosomes (children) not only one as in classical GA. We analyze the contribution of this method to reduce the execution time in the asynchronous and synchronous pipelines and also the possibility to a cheaper FPGA implementation, by using smaller populations. The full hardware architecture for a FPGA implementation to our target ALTERA development card is presented and analyzed.

  14. Parameter Optimization of Linear Quadratic Controller Based on Genetic Algorithm

    Institute of Scientific and Technical Information of China (English)

    LI Jimin; SHANG Chaoxuan; ZOU Minghu

    2007-01-01

    The selection of weighting matrix in design of the linear quadratic optimal controller is an important topic in the control theory. In this paper, an approach based on genetic algorithm is presented for selecting the weighting matrix for the optimal controller. Genetic algorithm is adaptive heuristic search algorithm premised on the evolutionary ideas of natural selection and genetic. In this algorithm, the fitness function is used to evaluate individuals and reproductive success varies with fitness. In the design of the linear quadratic optimal controller, the fitness function has relation to the anticipated step response of the system. Not only can the controller designed by this approach meet the demand of the performance indexes of linear quadratic controller, but also satisfy the anticipated step response of close-loop system. The method possesses a higher calculating efficiency and provides technical support for the optimal controller in engineering application. The simulation of a three-order single-input single-output (SISO) system has demonstrated the feasibility and validity of the approach.

  15. 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.

  16. 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.

  17. Optimal Polygonal Approximation of Digital Planar Curves Using Genetic Algorithm and Tabu Search

    Institute of Scientific and Technical Information of China (English)

    2000-01-01

    Three heuristic algorithms for optimal polygonal approximation of digital planar curves is presented.With Genetic Algorithm (GA), improved Genetic Algorithm (IGA) based on Pareto optimal solution and Tabu Search (TS), a near optimal polygonal approximation was obtained.Compared to the famous Teh-chin algorithm, our algorithms have obtained the approximated polygons with less number of vertices and less approximation error.Compared to the dynamic programming algorithm, the processing time of our algorithms are much less expensive.

  18. Scope of Gradient and Genetic Algorithms in Multivariable Function Optimization

    Science.gov (United States)

    Shaykhian, Gholam Ali; Sen, S. K.

    2007-01-01

    Global optimization of a multivariable function - constrained by bounds specified on each variable and also unconstrained - is an important problem with several real world applications. Deterministic methods such as the gradient algorithms as well as the randomized methods such as the genetic algorithms may be employed to solve these problems. In fact, there are optimization problems where a genetic algorithm/an evolutionary approach is preferable at least from the quality (accuracy) of the results point of view. From cost (complexity) point of view, both gradient and genetic approaches are usually polynomial-time; there are no serious differences in this regard, i.e., the computational complexity point of view. However, for certain types of problems, such as those with unacceptably erroneous numerical partial derivatives and those with physically amplified analytical partial derivatives whose numerical evaluation involves undesirable errors and/or is messy, a genetic (stochastic) approach should be a better choice. We have presented here the pros and cons of both the approaches so that the concerned reader/user can decide which approach is most suited for the problem at hand. Also for the function which is known in a tabular form, instead of an analytical form, as is often the case in an experimental environment, we attempt to provide an insight into the approaches focusing our attention toward accuracy. Such an insight will help one to decide which method, out of several available methods, should be employed to obtain the best (least error) output. *

  19. 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.

  20. Genetic analysis of growth curves using the SAEM algorithm

    Directory of Open Access Journals (Sweden)

    Lavielle Marc

    2006-11-01

    Full Text Available Abstract The analysis of nonlinear function-valued characters is very important in genetic studies, especially for growth traits of agricultural and laboratory species. Inference in nonlinear mixed effects models is, however, quite complex and is usually based on likelihood approximations or Bayesian methods. The aim of this paper was to present an efficient stochastic EM procedure, namely the SAEM algorithm, which is much faster to converge than the classical Monte Carlo EM algorithm and Bayesian estimation procedures, does not require specification of prior distributions and is quite robust to the choice of starting values. The key idea is to recycle the simulated values from one iteration to the next in the EM algorithm, which considerably accelerates the convergence. A simulation study is presented which confirms the advantages of this estimation procedure in the case of a genetic analysis. The SAEM algorithm was applied to real data sets on growth measurements in beef cattle and in chickens. The proposed estimation procedure, as the classical Monte Carlo EM algorithm, provides significance tests on the parameters and likelihood based model comparison criteria to compare the nonlinear models with other longitudinal methods.

  1. The genetic algorithm: A robust method for stress inversion

    Science.gov (United States)

    Thakur, Prithvi; Srivastava, Deepak C.; Gupta, Pravin K.

    2017-01-01

    The stress inversion of geological or geophysical observations is a nonlinear problem. In most existing methods, it is solved by linearization, under certain assumptions. These linear algorithms not only oversimplify the problem but also are vulnerable to entrapment of the solution in a local optimum. We propose the use of a nonlinear heuristic technique, the genetic algorithm, which searches the global optimum without making any linearizing assumption or simplification. The algorithm mimics the natural evolutionary processes of selection, crossover and mutation and, minimizes a composite misfit function for searching the global optimum, the fittest stress tensor. The validity and efficacy of the algorithm are demonstrated by a series of tests on synthetic and natural fault-slip observations in different tectonic settings and also in situations where the observations are noisy. It is shown that the genetic algorithm is superior to other commonly practised methods, in particular, in those tectonic settings where none of the principal stresses is directed vertically and/or the given data set is noisy.

  2. A New Method for Medical Image Clustering Using Genetic Algorithm

    Directory of Open Access Journals (Sweden)

    Akbar Shahrzad Khashandarag

    2013-01-01

    Full Text Available Segmentation is applied in medical images when the brightness of the images becomes weaker so that making different in recognizing the tissues borders. Thus, the exact segmentation of medical images is an essential process in recognizing and curing an illness. Thus, it is obvious that the purpose of clustering in medical images is the recognition of damaged areas in tissues. Different techniques have been introduced for clustering in different fields such as engineering, medicine, data mining and so on. However, there is no standard technique of clustering to present ideal results for all of the imaging applications. In this paper, a new method combining genetic algorithm and k-means algorithm is presented for clustering medical images. In this combined technique, variable string length genetic algorithm (VGA is used for the determination of the optimal cluster centers. The proposed algorithm has been compared with the k-means clustering algorithm. The advantage of the proposed method is the accuracy in selecting the optimal cluster centers compared with the above mentioned technique.

  3. 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

  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...

  5. Application layer multicast routing solution based on genetic algorithms

    Institute of Scientific and Technical Information of China (English)

    Peng CHENG; Qiufeng WU; Qionghai DAI

    2009-01-01

    Application layer multicast routing is a multi-objective optimization problem.Three routing con-straints,tree's cost,tree's balance and network layer load distribution are analyzed in this paper.The three fitness functions are used to evaluate a multicast tree on the three indexes respectively and one general fitness function is generated.A novel approach based on genetic algorithms is proposed.Numerical simulations show that,compared with geometrical routing rules,the proposed algorithm improve all three indexes,especially on cost and network layer load distribution indexes.

  6. Mass spectrometry cancer data classification using wavelets and genetic algorithm.

    Science.gov (United States)

    Nguyen, Thanh; Nahavandi, Saeid; Creighton, Douglas; Khosravi, Abbas

    2015-12-21

    This paper introduces a hybrid feature extraction method applied to mass spectrometry (MS) data for cancer classification. Haar wavelets are employed to transform MS data into orthogonal wavelet coefficients. The most prominent discriminant wavelets are then selected by genetic algorithm (GA) to form feature sets. The combination of wavelets and GA yields highly distinct feature sets that serve as inputs to classification algorithms. Experimental results show the robustness and significant dominance of the wavelet-GA against competitive methods. The proposed method therefore can be applied to cancer classification models that are useful as real clinical decision support systems for medical practitioners.

  7. A Dedicated Genetic Algorithm for Localization of Moving Magnetic Objects

    Directory of Open Access Journals (Sweden)

    Roger Alimi

    2015-09-01

    Full Text Available A dedicated Genetic Algorithm (GA has been developed to localize the trajectory of ferromagnetic moving objects within a bounded perimeter. Localization of moving ferromagnetic objects is an important tool because it can be employed in situations when the object is obscured. This work is innovative for two main reasons: first, the GA has been tuned to provide an accurate and fast solution to the inverse magnetic field equations problem. Second, the algorithm has been successfully tested using real-life experimental data. Very accurate trajectory localization estimations were obtained over a wide range of scenarios.

  8. 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.

  9. 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...... have to be dynamic, i.e. change their behavior during the course of an algorithm run. The implementation of the GA including the customized operators is freely available at http://svn.fysik.dtu. dk/projects/pga....

  10. 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.

  11. Genetic Algorithm based PID controller for Frequency Regulation Ancillary services

    Directory of Open Access Journals (Sweden)

    Sandeep Bhongade

    2010-12-01

    Full Text Available In this paper, the parameters of Proportional, Integral and Derivative (PID controller for Automatic Generation Control (AGC suitable in restructured power system is tuned according to Generic Algorithms (GAs based performance indices. The key idea of the proposed method is to use the fitness function based on Area Control Error (ACE. The functioning of the proposed Genetic Algorithm based PID (GAPID controller has been demonstrated on a 75-bus Indian power system network and the results have been compared with those obtained by using Least Square Minimization method.

  12. A New Optimization Approach for Maximizing the Photovoltaic Panel Power Based on Genetic Algorithm and Lagrange Multiplier Algorithm

    Directory of Open Access Journals (Sweden)

    Mahdi M. M. El-Arini

    2013-01-01

    Full Text Available In recent years, the solar energy has become one of the most important alternative sources of electric energy, so it is important to operate photovoltaic (PV panel at the optimal point to obtain the possible maximum efficiency. This paper presents a new optimization approach to maximize the electrical power of a PV panel. The technique which is based on objective function represents the output power of the PV panel and constraints, equality and inequality. First the dummy variables that have effect on the output power are classified into two categories: dependent and independent. The proposed approach is a multistage one as the genetic algorithm, GA, is used to obtain the best initial population at optimal solution and this initial population is fed to Lagrange multiplier algorithm (LM, then a comparison between the two algorithms, GA and LM, is performed. The proposed technique is applied to solar radiation measured at Helwan city at latitude 29.87°, Egypt. The results showed that the proposed technique is applicable.

  13. Optimal Feature Extraction for Discriminating Raman Spectra of Different Skin Samples using Statistical Methods and Genetic Algorithm

    Directory of Open Access Journals (Sweden)

    Zohreh Dehghani Bidgoli

    2011-06-01

    Full Text Available Introduction: Raman spectroscopy, that is a spectroscopic technique based on inelastic scattering of monochromatic light, can provide valuable information about molecular vibrations, so using this technique we can study molecular changes in a sample. Material and Methods: In this research, 153 Raman spectra obtained from normal and dried skin samples. Baseline and electrical noise were eliminated in the preprocessing stage with subsequent normalization of Raman spectra. Then, using statistical analysis and Genetic algorithm, optimal features for discrimination between these two classes have been searched.  In statistical analysis for choosing optimal features, T test, Bhattacharyya distance and entropy between two classes have been calculated. Seeing that T test can better discriminate these two classes so this method used for selecting the best features. Another time Genetic algorithm used for selecting optimal features, finally using these selected features and classifiers such as LDA, KNN, SVM and neural network, these two classes have been discriminated. Results: In comparison of classifiers results, under various strategies for selecting features and classifier, the best results obtained in combination of genetic algorithm in feature selection and SVM in classification. Finally using combination of genetic algorithm and SVM, we could discriminate normal and dried skin samples with accuracy of 90%, sensitivity of 89% and specificity of 91%. Discussion and Conclusion: According to obtained results, we can conclude that genetic algorithm demonstrates better performance than statistical analysis in selection of discriminating features of Raman spectra. In addition, results of this research illustrate the potential of Raman spectroscopy in study of different material effects on skin and skin diseases related to skin dehydration.

  14. A pipelined FPGA implementation of an encryption algorithm based on genetic algorithm

    Science.gov (United States)

    Thirer, Nonel

    2013-05-01

    With the evolution of digital data storage and exchange, it is essential to protect the confidential information from every unauthorized access. High performance encryption algorithms were developed and implemented by software and hardware. Also many methods to attack the cipher text were developed. In the last years, the genetic algorithm has gained much interest in cryptanalysis of cipher texts and also in encryption ciphers. This paper analyses the possibility to use the genetic algorithm as a multiple key sequence generator for an AES (Advanced Encryption Standard) cryptographic system, and also to use a three stages pipeline (with four main blocks: Input data, AES Core, Key generator, Output data) to provide a fast encryption and storage/transmission of a large amount of data.

  15. Resizing Technique-Based Hybrid Genetic Algorithm for Optimal Drift Design of Multistory Steel Frame Buildings

    Directory of Open Access Journals (Sweden)

    Hyo Seon Park

    2014-01-01

    Full Text Available Since genetic algorithm-based optimization methods are computationally expensive for practical use in the field of structural optimization, a resizing technique-based hybrid genetic algorithm for the drift design of multistory steel frame buildings is proposed to increase the convergence speed of genetic algorithms. To reduce the number of structural analyses required for the convergence, a genetic algorithm is combined with a resizing technique that is an efficient optimal technique to control the drift of buildings without the repetitive structural analysis. The resizing technique-based hybrid genetic algorithm proposed in this paper is applied to the minimum weight design of three steel frame buildings. To evaluate the performance of the algorithm, optimum weights, computational times, and generation numbers from the proposed algorithm are compared with those from a genetic algorithm. Based on the comparisons, it is concluded that the hybrid genetic algorithm shows clear improvements in convergence properties.

  16. 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,

  17. 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,

  18. A Genetic Algorithm for Delay-constrained Least-cost Multicast Routing

    Institute of Scientific and Technical Information of China (English)

    2000-01-01

    The delay-constrained least-cost multicast routing problem is introduced and then a related genetic algorithm is proposed. Finally, simulation results are shown to prove that the genetic algorithm is fast and effective.

  19. 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,

  20. 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,

  1. 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.

  2. Error analysis on heading determination via genetic algorithms

    Institute of Scientific and Technical Information of China (English)

    Zhong Bing; Xu Jiangning; Ma Heng

    2006-01-01

    A new error analysis method is presented via genetic algorithms for high precise heading determination model based on two total positioning stations (TPSs). The method has the ability to search all possible solution space by the genetic operators of elitist model and restriction. The result of analyzing the error of this model shows that the accuracy of this model is precise enough to meet the need of calibration for navigation systems on ship, and the search space is only 0.03% of the total search space, and the precision of heading determination is 4" in a general dock.

  3. Aligning multiple protein sequences by parallel hybrid genetic algorithm.

    Science.gov (United States)

    Nguyen, Hung Dinh; Yoshihara, Ikuo; Yamamori, Kunihito; Yasunaga, Moritoshi

    2002-01-01

    This paper presents a parallel hybrid genetic algorithm (GA) for solving the sum-of-pairs multiple protein sequence alignment. A new chromosome representation and its corresponding genetic operators are proposed. A multi-population GENITOR-type GA is combined with local search heuristics. It is then extended to run in parallel on a multiprocessor system for speeding up. Experimental results of benchmarks from the BAliBASE show that the proposed method is superior to MSA, OMA, and SAGA methods with regard to quality of solution and running time. It can be used for finding multiple sequence alignment as well as testing cost functions.

  4. 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.

  5. Power Transmission System Vulnerability Assessment Using Genetic Algorithm

    Directory of Open Access Journals (Sweden)

    E. Karimi

    2012-11-01

    Full Text Available Recent blackouts in power systems have shown the necessity of vulnerability assessment. Among all factors, transmission system components have a more important role. Power system vulnerability assessment could capture cascading outages which result in large blackouts and is an effective tool for power system engineers for defining power system bottlenecks and weak points. In this paper a new method based on fault chains concept is developed which uses new measures. Genetic algorithm with an effective structure is used for finding vulnerable branches in a practical power transmission system. Analytic hierarchy process is a technique used to determine the weighting factors in fitness function of genetic algorithm. Finally, the numerical results for Isfahan Regional Electric Company are presented which verifies the effectiveness and precision of the proposed method according to the practical expriments.

  6. Assigning Task by Parallel Genetic Algorithm Based on PVM

    Institute of Scientific and Technical Information of China (English)

    2001-01-01

    Genetic algorithm has been proposed to solve the problem of taskassignment. Ho wever, it has some drawbacks, e.g., it often takes a long time to find an optima l solution, and the success rate is low. To overcome these problems, a new coars e-grained parallel genetic algorithm with the scheme of central migration is pr e sented, which exploits isolated sub-populations. The new approach has been impl e mented in the PVM environment and has been evaluated on a workstation network fo r solving the task assignment problem. The results show that it not only signifi cantly improves the result quality but also increases the speed for getting best solution.

  7. A sustainable genetic algorithm for satellite resource allocation

    Science.gov (United States)

    Abbott, R. J.; Campbell, M. L.; Krenz, W. C.

    1995-01-01

    A hybrid genetic algorithm is used to schedule tasks for 8 satellites, which can be modelled as a robot whose task is to retrieve objects from a two dimensional field. The objective is to find a schedule that maximizes the value of objects retrieved. Typical of the real-world tasks to which this corresponds is the scheduling of ground contacts for a communications satellite. An important feature of our application is that the amount of time available for running the scheduler is not necessarily known in advance. This requires that the scheduler produce reasonably good results after a short period but that it also continue to improve its results if allowed to run for a longer period. We satisfy this requirement by developing what we call a sustainable genetic algorithm.

  8. 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.

  9. A New Neuro-Fuzzy Adaptive Genetic Algorithm

    Institute of Scientific and Technical Information of China (English)

    ZHU Lili; ZHANG Huanchun; JING Yazhi

    2003-01-01

    Novel neuro-fuzzy techniques are used to dynamically control parameter settings of genetic algorithms (GAs). The benchmark routine is an adaptive genetic algorithm (AGA) that uses a fuzzy knowledge-based system to control GA parameters. The self-learning ability of the cerebellar model ariculation controller(CMAC) neural network makes it possible for on-line learning the knowledge on GAs throughout the run. Automatically designing and tuning the fuzzy knowledge-base system, neurofuzzy techniques based on CMAC can find the optimized fuzzy system for AGA by the renhanced learning method. The Results from initial experiments show a Dynamic Parametric AGA system designed by the proposed automatic method and indicate the general applicability of the neuro-fuzzy AGA to a wide range of combinatorial optimization.

  10. Quantum control using genetic algorithms in quantum communication: superdense coding

    Science.gov (United States)

    Domínguez-Serna, Francisco; Rojas, Fernando

    2015-06-01

    We present a physical example model of how Quantum Control with genetic algorithms is applied to implement the quantum superdense code protocol. We studied a model consisting of two quantum dots with an electron with spin, including spin-orbit interaction. The electron and the spin get hybridized with the site acquiring two degrees of freedom, spin and charge. The system has tunneling and site energies as time dependent control parameters that are optimized by means of genetic algorithms to prepare a hybrid Bell-like state used as a transmission channel. This state is transformed to obtain any state of the four Bell basis as required by superdense protocol to transmit two bits of classical information. The control process protocol is equivalent to implement one of the quantum gates in the charge subsystem. Fidelities larger than 99.5% are achieved for the hybrid entangled state preparation and the superdense operations.

  11. 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.

  12. 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.

  13. Load Flow Analysis Using Real Coded Genetic Algorithm

    Directory of Open Access Journals (Sweden)

    Himakar Udatha

    2014-02-01

    Full Text Available This paper presents a Real Coded Genetic Algorithm (RCGA for finding the load flow solution of electrical power systems. The proposed method is based on the minimization of the real and reactive power mismatches at various buses. The traditional methods such as Gauss-Seidel method and Newton-Raphson (NR method have certain drawbacks under abnormal operating condition. In order to overcome these problems, the load flow solution based on Real Coded Genetic Algorithm (RCGA is presented in this paper. Two cross over techniques, Arithmetic crossover and heuristic crossover are used to solve the power flow problem. The proposed method is applied for 3-bus, 5-bus and 6-bus systems and the results are presented.

  14. 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.

  15. 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.

  16. Genetic algorithm for network cost minimization using threshold based discounting

    Directory of Open Access Journals (Sweden)

    Hrvoje Podnar

    2003-01-01

    Full Text Available We present a genetic algorithm for heuristically solving a cost minimization problem applied to communication networks with threshold based discounting. The network model assumes that every two nodes can communicate and offers incentives to combine flow from different sources. Namely, there is a prescribed threshold on every link, and if the total flow on a link is greater than the threshold, the cost of this flow is discounted by a factor α. A heuristic algorithm based on genetic strategy is developed and applied to a benchmark set of problems. The results are compared with former branch and bound results using the CPLEX® solver. For larger data instances we were able to obtain improved solutions using less CPU time, confirming the effectiveness of our heuristic approach.

  17. 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.

  18. Healing Temperature of Hybrid Structures Based on Genetic Algorithm

    Institute of Scientific and Technical Information of China (English)

    赵中伟; 陈志华; 刘红波

    2016-01-01

    The healing temperature of suspen-dome with stacked arches(SDSA)and arch-supported single-layer lattice shell structures was investigated based on the genetic algorithm. The temperature field of arch under solar radiation was derived by FLUENT to investigate the influence of solar radiation on the determination of the healing temperature. Moreover, a multi-scale model was established to apply the complex temperature field under solar radiation. The change in the mechanical response of these two kinds of structures with the healing temperature was discussed. It can be concluded that solar radiation has great influence on the healing temperature, and the genetic algorithm can be effectively used in the optimization of the healing temperature for hybrid structures.

  19. Users guide to the PGAPack parallel genetic algorithm library

    Energy Technology Data Exchange (ETDEWEB)

    Levine, D.

    1996-01-01

    PGAPack is a parallel genetic algorithm library that is intended to provide most capabilities desired in a genetic algorithm package, in an integrated, seamless, and portable manner. Key features of PGAPack are as follows: Ability to be called from Fortran or C. Executable on uniprocessors, multiprocessors, multicomputers, and workstation networks. Binary-, integer-, real-, and character-valued native data types. Object-oriented data structure neutral design. Parameterized population replacement. Multiple choices for selection, crossover, and mutation operators. Easy integration of hill-climbing heuristics. Easy-to-use interface for novice and application users. Multiple levels of access for expert users. Full extensibility to support custom operators and new data types. Extensive debugging facilities. Large set of example problems.

  20. 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.