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A Min-Max Genetic Algorithm with Alternating Multiple Sorting for Solving Constrained Problems Timo Mantere Department of Electrical Engineering and Automation University of Vaasa FIN[removed]Vaasa
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Document Date: 2006-10-19 04:44:00

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File Size: 80,79 KB

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The MIT Press / Advanced Manufacturing / Artificial Systems / Physics Publishing / HP / Intelligent Systems / /

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Netherlands / Tunisia / United States / Canada / /

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IEEE Service Center / Institute of Physics Publishing / University of Alberta / Automation University / /

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a lot of infeasible solutions / infeasible solutions / evolutionary algorithms / search direction / min-max genetic algorithm / genetic algorithm / unfeasible solutions / steady-state genetic algorithm / search space / feasible and infeasible solutions / feasible solutions / /

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set 15 / /

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Electrical Engineering and Automation University of Vaasa FIN-65101 Vaasa / MIT / University of Alberta / Congress / Solving Constrained Problems Timo Mantere Department of Electrical Engineering / /

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Li / Runarsson / /

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author / /

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Alberta / Washington / Georgia / Massachusetts / Arkansas / /

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IEEE Transactions on Evolutionary Computation / /

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genetic algorithm / same genetic algorithm / steady-state genetic algorithm / min-max genetic algorithm / 1.1 Genetic Algorithms Genetic algorithms / six Gamut Mapping Algorithms / genotype / /

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