Evolutionary Computation
Computational intelligence is a general term for a class of algorithms designed by nature's wisdom and human intelligence. Computer scientists have proposed many computational intelligence algorithms with heuristic features. These algorithms either mimic the evolutionary processes of the biological...
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| Asıl Yazarlar: | , |
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| Materyal Türü: | Online |
| Dil: | İngilizce |
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MDPI - Multidisciplinary Digital Publishing Institute
2021
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| Konular: | |
| Online Erişim: | 42682 |
| Etiketler: |
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| _version_ | 1869516831313625088 |
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| author | Alavi, Amir Wang, Gai-Ge |
| author_browse | Alavi, Amir Wang, Gai-Ge |
| author_facet | Alavi, Amir Wang, Gai-Ge |
| author_sort | Alavi, Amir |
| collection | Directory of Open Access Books |
| description | Computational intelligence is a general term for a class of algorithms designed by nature's wisdom and human intelligence. Computer scientists have proposed many computational intelligence algorithms with heuristic features. These algorithms either mimic the evolutionary processes of the biological world, mimic the physiological structure and bodily functions of the organism, |
| format | Online |
| id | doab-20.500.12854ir-47147 |
| institution | Directory of Open Access Books |
| language | eng |
| publishDate | 2021 |
| publishDateRange | 2021 |
| publishDateSort | 2021 |
| publisher | MDPI - Multidisciplinary Digital Publishing Institute |
| publisherStr | MDPI - Multidisciplinary Digital Publishing Institute |
| record_format | ojs |
| spelling | doab-20.500.12854ir-471472024-04-11T15:11:29Z Evolutionary Computation Alavi, Amir Wang, Gai-Ge TA1-2040 T1-995 individual updating strategy integrated design global optimum flexible job shop scheduling problem whale optimization algorithm EHO bat algorithm with multiple strategy coupling (mixBA) multi-objective DV-Hop localization algorithm optimization rock types variable neighborhood search biology average iteration times CEC2013 benchmarks slicing tree structure firefly algorithm (FA) benchmark single loop evolutionary computation memetic algorithm normal cloud model 0-1 knapsack problems elite strategy diversity maintenance material handling path artificial bee colony algorithm (ABC) urban design entropy evolutionary algorithms (EAs) monarch butterfly optimization numerical simulation architecture set-union knapsack problem Wilcoxon test convolutional neural network global position updating operator particle swarm optimization computation minimum load coloring topology structure adaptive multi-swarm minimum total dominating set mutation operation shape grammar greedy optimization algorithm ?-Hilbert space genetic algorithm large scale optimization large-scale optimization NSGA-II-DV-Hop constrained optimization problems (COPs) first-arrival picking transfer function SPEA 2 stochastic ranking (SR) wireless sensor networks (WSNs) acceleration search convergence point fuzzy c-means evolutionary algorithm success rates Artificial bee colony particle swarm optimizer random weight range detection adaptive weight large-scale automatic identification cloud model swarm intelligence evolutionary multi-objective optimization DV-Hop algorithm bat algorithm (BA) Friedman test quantum uncertainty property facility layout design local search deep learning Y conditional cloud generator benchmark functions discrete algorithm dispatching rule DE algorithm nonlinear convergence factor energy-efficient job shop scheduling t-test evolution dimension learning global optimization confidence term elephant herding optimization moth search algorithm evolutionary thema EDItEUR::T Technology, Engineering, Agriculture, Industrial processes::TB Technology: general issues::TBX History of engineering and technology Computational intelligence is a general term for a class of algorithms designed by nature's wisdom and human intelligence. Computer scientists have proposed many computational intelligence algorithms with heuristic features. These algorithms either mimic the evolutionary processes of the biological world, mimic the physiological structure and bodily functions of the organism, 2021-02-11T13:12:53Z 2021-02-11T13:12:53Z 2019-12-09 11:49:16 2019 book 42682 9783039219285 9783039219292 https://directory.doabooks.org/handle/20.500.12854/47147 eng application/octet-stream Attribution-NonCommercial-NoDerivatives 4.0 International https://mdpi.com/books/pdfview/book/1860 MDPI - Multidisciplinary Digital Publishing Institute 10.3390/books978-3-03921-929-2 10.3390/books978-3-03921-929-2 46cabcaa-dd94-4bfe-87b4-55023c1b36d0 9783039219285 9783039219292 424 open access |
| spellingShingle | TA1-2040 T1-995 individual updating strategy integrated design global optimum flexible job shop scheduling problem whale optimization algorithm EHO bat algorithm with multiple strategy coupling (mixBA) multi-objective DV-Hop localization algorithm optimization rock types variable neighborhood search biology average iteration times CEC2013 benchmarks slicing tree structure firefly algorithm (FA) benchmark single loop evolutionary computation memetic algorithm normal cloud model 0-1 knapsack problems elite strategy diversity maintenance material handling path artificial bee colony algorithm (ABC) urban design entropy evolutionary algorithms (EAs) monarch butterfly optimization numerical simulation architecture set-union knapsack problem Wilcoxon test convolutional neural network global position updating operator particle swarm optimization computation minimum load coloring topology structure adaptive multi-swarm minimum total dominating set mutation operation shape grammar greedy optimization algorithm ?-Hilbert space genetic algorithm large scale optimization large-scale optimization NSGA-II-DV-Hop constrained optimization problems (COPs) first-arrival picking transfer function SPEA 2 stochastic ranking (SR) wireless sensor networks (WSNs) acceleration search convergence point fuzzy c-means evolutionary algorithm success rates Artificial bee colony particle swarm optimizer random weight range detection adaptive weight large-scale automatic identification cloud model swarm intelligence evolutionary multi-objective optimization DV-Hop algorithm bat algorithm (BA) Friedman test quantum uncertainty property facility layout design local search deep learning Y conditional cloud generator benchmark functions discrete algorithm dispatching rule DE algorithm nonlinear convergence factor energy-efficient job shop scheduling t-test evolution dimension learning global optimization confidence term elephant herding optimization moth search algorithm evolutionary thema EDItEUR::T Technology, Engineering, Agriculture, Industrial processes::TB Technology: general issues::TBX History of engineering and technology Alavi, Amir Wang, Gai-Ge Evolutionary Computation |
| title | Evolutionary Computation |
| title_full | Evolutionary Computation |
| title_fullStr | Evolutionary Computation |
| title_full_unstemmed | Evolutionary Computation |
| title_short | Evolutionary Computation |
| title_sort | evolutionary computation |
| topic | TA1-2040 T1-995 individual updating strategy integrated design global optimum flexible job shop scheduling problem whale optimization algorithm EHO bat algorithm with multiple strategy coupling (mixBA) multi-objective DV-Hop localization algorithm optimization rock types variable neighborhood search biology average iteration times CEC2013 benchmarks slicing tree structure firefly algorithm (FA) benchmark single loop evolutionary computation memetic algorithm normal cloud model 0-1 knapsack problems elite strategy diversity maintenance material handling path artificial bee colony algorithm (ABC) urban design entropy evolutionary algorithms (EAs) monarch butterfly optimization numerical simulation architecture set-union knapsack problem Wilcoxon test convolutional neural network global position updating operator particle swarm optimization computation minimum load coloring topology structure adaptive multi-swarm minimum total dominating set mutation operation shape grammar greedy optimization algorithm ?-Hilbert space genetic algorithm large scale optimization large-scale optimization NSGA-II-DV-Hop constrained optimization problems (COPs) first-arrival picking transfer function SPEA 2 stochastic ranking (SR) wireless sensor networks (WSNs) acceleration search convergence point fuzzy c-means evolutionary algorithm success rates Artificial bee colony particle swarm optimizer random weight range detection adaptive weight large-scale automatic identification cloud model swarm intelligence evolutionary multi-objective optimization DV-Hop algorithm bat algorithm (BA) Friedman test quantum uncertainty property facility layout design local search deep learning Y conditional cloud generator benchmark functions discrete algorithm dispatching rule DE algorithm nonlinear convergence factor energy-efficient job shop scheduling t-test evolution dimension learning global optimization confidence term elephant herding optimization moth search algorithm evolutionary thema EDItEUR::T Technology, Engineering, Agriculture, Industrial processes::TB Technology: general issues::TBX History of engineering and technology |
| topic_facet | TA1-2040 T1-995 individual updating strategy integrated design global optimum flexible job shop scheduling problem whale optimization algorithm EHO bat algorithm with multiple strategy coupling (mixBA) multi-objective DV-Hop localization algorithm optimization rock types variable neighborhood search biology average iteration times CEC2013 benchmarks slicing tree structure firefly algorithm (FA) benchmark single loop evolutionary computation memetic algorithm normal cloud model 0-1 knapsack problems elite strategy diversity maintenance material handling path artificial bee colony algorithm (ABC) urban design entropy evolutionary algorithms (EAs) monarch butterfly optimization numerical simulation architecture set-union knapsack problem Wilcoxon test convolutional neural network global position updating operator particle swarm optimization computation minimum load coloring topology structure adaptive multi-swarm minimum total dominating set mutation operation shape grammar greedy optimization algorithm ?-Hilbert space genetic algorithm large scale optimization large-scale optimization NSGA-II-DV-Hop constrained optimization problems (COPs) first-arrival picking transfer function SPEA 2 stochastic ranking (SR) wireless sensor networks (WSNs) acceleration search convergence point fuzzy c-means evolutionary algorithm success rates Artificial bee colony particle swarm optimizer random weight range detection adaptive weight large-scale automatic identification cloud model swarm intelligence evolutionary multi-objective optimization DV-Hop algorithm bat algorithm (BA) Friedman test quantum uncertainty property facility layout design local search deep learning Y conditional cloud generator benchmark functions discrete algorithm dispatching rule DE algorithm nonlinear convergence factor energy-efficient job shop scheduling t-test evolution dimension learning global optimization confidence term elephant herding optimization moth search algorithm evolutionary thema EDItEUR::T Technology, Engineering, Agriculture, Industrial processes::TB Technology: general issues::TBX History of engineering and technology |
| url | 42682 |
| work_keys_str_mv | AT alaviamir evolutionarycomputation AT wanggaige evolutionarycomputation |