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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| Главный автор: | |
|---|---|
| Формат: | Online |
| Язык: | английский |
| Опубликовано: |
KIT Scientific Publishing
2021
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| Предметы: | |
| Online-ссылка: | 34679 |
| Метки: |
Нет меток, Требуется 1-ая метка записи!
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| _version_ | 1869519857796513792 |
|---|---|
| author | Schmidt, Christian |
| author_browse | Schmidt, Christian |
| author_facet | Schmidt, Christian |
| author_sort | Schmidt, Christian |
| collection | Directory of Open Access Books |
| description | 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. |
| format | Online |
| id | doab-20.500.12854ir-47148 |
| institution | Directory of Open Access Books |
| language | eng |
| publishDate | 2021 |
| publishDateRange | 2021 |
| publishDateSort | 2021 |
| publisher | KIT Scientific Publishing |
| publisherStr | KIT Scientific Publishing |
| record_format | ojs |
| spelling | doab-20.500.12854ir-471482023-12-20T18:40:46Z Evolutionary computation in stochastic environments Schmidt, Christian QA75.5-76.95 Evolutionary Algorithm Uncertainty Simulation based optimization Bayes Ranking & Selection Genetic Algorithms bic Book Industry Communication::U Computing & information technology::UY Computer science 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. 2021-02-11T13:12:56Z 2021-02-11T13:12:56Z 2019-07-30 20:01:58 2007 book 34679 9783866441286 https://directory.doabooks.org/handle/20.500.12854/47148 eng image/jpeg Attribution-NonCommercial-NoDerivatives 4.0 International https://www.ksp.kit.edu/9783866441286 KIT Scientific Publishing 10.5445/KSP/1000006634 10.5445/KSP/1000006634 68fffc18-8f7b-44fa-ac7e-0b7d7d979bd2 9783866441286 VII, 128 p. open access |
| spellingShingle | QA75.5-76.95 Evolutionary Algorithm Uncertainty Simulation based optimization Bayes Ranking & Selection Genetic Algorithms bic Book Industry Communication::U Computing & information technology::UY Computer science Schmidt, Christian Evolutionary computation in stochastic environments |
| title | Evolutionary computation in stochastic environments |
| title_full | Evolutionary computation in stochastic environments |
| title_fullStr | Evolutionary computation in stochastic environments |
| title_full_unstemmed | Evolutionary computation in stochastic environments |
| title_short | Evolutionary computation in stochastic environments |
| title_sort | evolutionary computation in stochastic environments |
| topic | QA75.5-76.95 Evolutionary Algorithm Uncertainty Simulation based optimization Bayes Ranking & Selection Genetic Algorithms bic Book Industry Communication::U Computing & information technology::UY Computer science |
| topic_facet | QA75.5-76.95 Evolutionary Algorithm Uncertainty Simulation based optimization Bayes Ranking & Selection Genetic Algorithms bic Book Industry Communication::U Computing & information technology::UY Computer science |
| url | 34679 |
| work_keys_str_mv | AT schmidtchristian evolutionarycomputationinstochasticenvironments |