Learning with genetic algorithms: An overview

DeJong, Kenneth

doi:10.1007/bf00113894

Cited by 358 publications

(153 citation statements)

References 12 publications

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“…Over the years GA which is considered to be an inductive searching technique demonstrated substantial improvement [14]. The Feature selection using genetic algorithms are considered to be an excellent choice for improving the performance of the classification system [15].…”

Section: Genetic Algorithmmentioning

confidence: 99%

Face Recognition based on a Hybrid Meta-heuristic Feature Selection Algorithm

Arivalagan¹,

Venkatachalapathy²

2012

IJCA

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For the past few years, a number of new face recognition techniques have been proposed. Always it is a big unanswered question among face recognition researchers about which method or technique will have better performance. In this study an approach to recognize known faces based on Eigen vectors and a hybrid Meta-heuristic feature selection algorithm is proposed. The eigenvectors which are covariance matrix of the face images together describes the difference between face images. Face recognition problem is viewed as a two dimensional recognition problem. Initially the face images are projected in to face space and using Principal component analysis the eigenvectors with high Eigenvalues are extracted to reduce the dimension of the feature vector. Further to select the best feature vectors which increase the classification accuracy is selected by using a hybrid metaheuristic algorithm using Genetic algorithm (GA) and Bacteria Foraging Optimization (BFO). In this study the Support vector machine (SVM) and Back propagation neural network (BPNN) are used for classification. The classifiers are trained and tested separately using the frontal face images taken from AT&T database. The SVM and BPNN produces an average classification accuracy of 82.6% and 83.28% respectively.

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Section: Genetic Algorithmmentioning

confidence: 99%

Face Recognition based on a Hybrid Meta-heuristic Feature Selection Algorithm

Arivalagan¹,

Venkatachalapathy²

2012

IJCA

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“…Classically, two genetic learning approaches, adopted from the ÿeld of genetic based machine learning systems, have been used: the Michigan and Pittsburgh approaches [20]. In the past few years, some authors have designed several GFRBSs following a new learning model, the IRL approach [24].…”

Section: The Iterative Rule Learning Approachmentioning

confidence: 99%

Hybridizing genetic algorithms with sharing scheme and evolution strategies for designing approximate fuzzy rule-based systems

Cordón¹,

Herrera²

2001

Fuzzy Sets and Systems

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Genetic algorithms and evolution strategies are combined in order to build a multi-stage hybrid evolutionary algorithm for learning constrained approximate Mamdani-type knowledge bases from examples. The genetic algorithm niche concept is used in two of the three stages composing the learning process with the purpose of improving the accuracy of the designed fuzzy rule-based systems. The proposed genetic fuzzy rule-based system is used to solve an electrical engineering problem and the results obtained are compared with other methods presenting di erent characteristics.

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“…If that space is well-understood and contains structure that can be exploited by special-purpose search techniques, the use of genetic algorithms is generally computationally less efficient. If the space to be searched is not so well understood and relatively unstructured, and if an effective GA representation of that space can be developed, then GAs provide a surprisingly powerful search heuristic for large, complex spaces (Holland, 1985;De Jong, 1988;Goldberg, 1989).…”

Section: Introductionmentioning

confidence: 99%

Introduction

Kenneth

1990

Mach Learn

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Learning with genetic algorithms: An overview

Cited by 358 publications

References 12 publications

Face Recognition based on a Hybrid Meta-heuristic Feature Selection Algorithm

Face Recognition based on a Hybrid Meta-heuristic Feature Selection Algorithm

Hybridizing genetic algorithms with sharing scheme and evolution strategies for designing approximate fuzzy rule-based systems

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