Evolutionary computation in stochastic environments

This book develops efficient methods for the application of Evolutionary Algorithms on stochastic problems. To achieve this, procedures for statistical selection are systematically analyzed with respect to different measures and significantly improved. It is shown how to adapt one of the best proced...

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Bibliographic Details
Main Author: Schmidt, Christian (auth)
Format: Book Chapter
Published: KIT Scientific Publishing 2007
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Online Access:Get Fullteks
DOAB: description of the publication
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020 |a KSP/1000006634 
020 |a 9783866441286 
024 7 |a 10.5445/KSP/1000006634  |c doi 
041 0 |a English 
042 |a dc 
100 1 |a Schmidt, Christian  |4 auth 
245 1 0 |a Evolutionary computation in stochastic environments 
260 |b KIT Scientific Publishing  |c 2007 
300 |a 1 electronic resource (VII, 128 p. p.) 
506 0 |a Open Access  |2 star  |f Unrestricted online access 
520 |a This book develops efficient methods for the application of Evolutionary Algorithms on stochastic problems. To achieve this, procedures for statistical selection are systematically analyzed with respect to different measures and significantly improved. It is shown how to adapt one of the best procedures for the needs of Evolutionary Algorithms and Evolutionary operators for efficient implementation in stochastic environments are identified. 
540 |a Creative Commons  |f https://creativecommons.org/licenses/by-nc-nd/4.0/  |2 cc  |4 https://creativecommons.org/licenses/by-nc-nd/4.0/ 
546 |a English 
653 |a Evolutionary Algorithm 
653 |a Uncertainty 
653 |a Simulation based optimization 
653 |a Bayes 
653 |a Ranking & Selection 
653 |a Genetic Algorithms 
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856 4 0 |a www.oapen.org  |u https://directory.doabooks.org/handle/20.500.12854/47148  |7 0  |z DOAB: description of the publication