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Automated Operator Selection on Genetic Algorithms
2005Genetic Algorithms (GAs) have proven to be a useful means of finding optimal or near optimal solutions to hard problems that are difficult to solve by other means. However, determining which crossover and mutation operator is best to use for a specific problem can be a complex task requiring much trial and error. Furthermore, different operators may be
Fredrik G. Hilding, Koren Ward
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A genetic algorithm for feature selection in gait analysis
2016 IEEE Congress on Evolutionary Computation (CEC), 2016This paper deals with the opportunity of extracting useful information from medical data retrieved directly from a stereophotogrammetric system applied to gait analysis, which aims at controlling movements of patients affected by neurological diseases. The proposed approach is intended to a feature selection procedure as an optimization strategy based ...
ALTILIO, ROSA +4 more
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Formal models of selection in genetic algorithms
1994In this paper three formal models of selection operators (two known from the literature and one newly porposed) for genetic algorithms, used to learn structured concepts descriptions containing small disjuncts, are presented. The evolution of a population, according to these operators, with a generation gap equal to or less than one, is investigated in
Attilio Giordana +2 more
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Selective breeding in a multiobjective genetic algorithm
1998This paper describes an investigation of the efficacy of various elitist selection strategies in a multiobjective Genetic Algorithm implementation, with parents being selected both from the current population and from the archive record of nondominated solutions encountered during search.
Geoffrey T. Parks, I. Miller
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GrC model in Genetic Algorithm: Artificial Selection Algorithm
2008 IEEE International Conference on Granular Computing, 2008Genetic Algorithm (GA), a programming technique that mimics natural evolution as a problem-solving strategy, has become popular since its appearance. It keeps the properties similar to natural selection systems. Many improved GAs has been proposed, however, natural selection essence is not changed.
Zehua Chen 0001 +3 more
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Feature subset selection using a genetic algorithm
IEEE Intelligent Systems, 1998Practical pattern-classification and knowledge-discovery problems require the selection of a subset of attributes or features to represent the patterns to be classified. The authors' approach uses a genetic algorithm to select such subsets, achieving multicriteria optimization in terms of generalization accuracy and costs associated with the features.
Jihoon Yang, Vasant G. Honavar
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Biologically Inspired Parent Selection in Genetic Algorithms
Annals of Operations Research, 2019zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Zvi Drezner, Taly Dawn Drezner
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Parallel genetic algorithm with fading selection
International Journal of Computer Applications in Technology, 2014This work presents a novel approach to designing the parallel genetic algorithm GA with fading selection for the solving of the problem of the shareholder value maximisation of an oil company. The algorithm based on the dynamical interaction of synchronised processes, which are interdependent GAs having own separate evolutions of their populations. The
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On the Influence of Selection Schemes on the Genetic Diversity in Genetic Algorithms
2009This paper discusses some aspects of the general convergence behavior of genetic algorithms. Careful attention is given to how different selection strategies influence the progress of genetic diversity in populations. For being able to observe genetic diversity over time measures are introduced for estimating pairwise similarities as well as ...
Michael Affenzeller +3 more
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Genetic Algorithm as an Attributes Selection Tool for Learning Algorithms
2004Learning algorithms, as NN or C4.5 require adequate sets of examples. In the paper we present the usability of genetic algorithms for selection significant features. Fitness of individuals is calculated on the basis of classification quality using NN or C4.5 algorithm.
Halina Kwasnicka, Piotr Orski
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