Evolutionary Algorithms in Intelligent Systems
Evolutionary algorithms and metaheuristics are widely used to provide efficient and effective approximate solutions to computationally hard optimization problems. With the widespread use of intelligent systems in recent years, evolutionary algorithms have been applied, beyond classical optimization...
Zapisane w:
| Format: | Online |
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| Język: | angielski |
| Wydane: |
MDPI - Multidisciplinary Digital Publishing Institute
2021
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| Hasła przedmiotowe: | |
| Dostęp online: | ONIX_20210501_9783039436118_1137 |
| Etykiety: |
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| _version_ | 1869516508341731328 |
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| collection | Directory of Open Access Books |
| description | Evolutionary algorithms and metaheuristics are widely used to provide efficient and effective approximate solutions to computationally hard optimization problems. With the widespread use of intelligent systems in recent years, evolutionary algorithms have been applied, beyond classical optimization problems, to AI system parameter optimization and the design of artificial neural networks and feature selection in machine learning systems. This volume will present recent results of applications of the most successful metaheuristics, from differential evolution and particle swarm optimization to artificial neural networks, loT allocation, and multi-objective optimization problems. It will also provide a broad view of the role and the potential of evolutionary algorithms as service components in Al systems. |
| format | Online |
| id | doab-20.500.12854ir-69391 |
| 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-693912024-03-30T12:51:02Z Evolutionary Algorithms in Intelligent Systems Milani, Alfredo Carpi, Arturo Poggioni, Valentina multi-objective optimization problems particle swarm optimization (PSO) Gaussian mutation improved learning strategy big data interval concept lattice horizontal union sequence traversal evolutionary algorithms multi-objective optimization parameter puning parameter analysis particle swarm optimization differential evolution global continuous optimization wireless sensor networks task allocation stochastic optimization social network optimization memetic particle swarm optimization adaptive local search operator co-evolution PSO formal methods in evolutionary algorithms self-adaptive differential evolutionary algorithms constrained optimization ensemble of constraint handling techniques hybrid algorithms association rules mining algorithm vertical union neuroevolution neural networks n/a thema EDItEUR::K Economics, Finance, Business and Management::KN Industry and industrial studies::KNT Media, entertainment, information and communication industries::KNTX Information technology industries Evolutionary algorithms and metaheuristics are widely used to provide efficient and effective approximate solutions to computationally hard optimization problems. With the widespread use of intelligent systems in recent years, evolutionary algorithms have been applied, beyond classical optimization problems, to AI system parameter optimization and the design of artificial neural networks and feature selection in machine learning systems. This volume will present recent results of applications of the most successful metaheuristics, from differential evolution and particle swarm optimization to artificial neural networks, loT allocation, and multi-objective optimization problems. It will also provide a broad view of the role and the potential of evolutionary algorithms as service components in Al systems. 2021-05-01T15:48:33Z 2021-05-01T15:48:33Z 2020 book ONIX_20210501_9783039436118_1137 9783039436118 9783039436125 https://directory.doabooks.org/handle/20.500.12854/69391 eng application/octet-stream Attribution 4.0 International https://mdpi.com/books/pdfview/book/3184 https://mdpi.com/books/pdfview/book/3184 MDPI - Multidisciplinary Digital Publishing Institute 10.3390/books978-3-03943-612-5 10.3390/books978-3-03943-612-5 46cabcaa-dd94-4bfe-87b4-55023c1b36d0 9783039436118 9783039436125 144 Basel, Switzerland open access |
| spellingShingle | multi-objective optimization problems particle swarm optimization (PSO) Gaussian mutation improved learning strategy big data interval concept lattice horizontal union sequence traversal evolutionary algorithms multi-objective optimization parameter puning parameter analysis particle swarm optimization differential evolution global continuous optimization wireless sensor networks task allocation stochastic optimization social network optimization memetic particle swarm optimization adaptive local search operator co-evolution PSO formal methods in evolutionary algorithms self-adaptive differential evolutionary algorithms constrained optimization ensemble of constraint handling techniques hybrid algorithms association rules mining algorithm vertical union neuroevolution neural networks n/a thema EDItEUR::K Economics, Finance, Business and Management::KN Industry and industrial studies::KNT Media, entertainment, information and communication industries::KNTX Information technology industries Evolutionary Algorithms in Intelligent Systems |
| title | Evolutionary Algorithms in Intelligent Systems |
| title_full | Evolutionary Algorithms in Intelligent Systems |
| title_fullStr | Evolutionary Algorithms in Intelligent Systems |
| title_full_unstemmed | Evolutionary Algorithms in Intelligent Systems |
| title_short | Evolutionary Algorithms in Intelligent Systems |
| title_sort | evolutionary algorithms in intelligent systems |
| topic | multi-objective optimization problems particle swarm optimization (PSO) Gaussian mutation improved learning strategy big data interval concept lattice horizontal union sequence traversal evolutionary algorithms multi-objective optimization parameter puning parameter analysis particle swarm optimization differential evolution global continuous optimization wireless sensor networks task allocation stochastic optimization social network optimization memetic particle swarm optimization adaptive local search operator co-evolution PSO formal methods in evolutionary algorithms self-adaptive differential evolutionary algorithms constrained optimization ensemble of constraint handling techniques hybrid algorithms association rules mining algorithm vertical union neuroevolution neural networks n/a thema EDItEUR::K Economics, Finance, Business and Management::KN Industry and industrial studies::KNT Media, entertainment, information and communication industries::KNTX Information technology industries |
| topic_facet | multi-objective optimization problems particle swarm optimization (PSO) Gaussian mutation improved learning strategy big data interval concept lattice horizontal union sequence traversal evolutionary algorithms multi-objective optimization parameter puning parameter analysis particle swarm optimization differential evolution global continuous optimization wireless sensor networks task allocation stochastic optimization social network optimization memetic particle swarm optimization adaptive local search operator co-evolution PSO formal methods in evolutionary algorithms self-adaptive differential evolutionary algorithms constrained optimization ensemble of constraint handling techniques hybrid algorithms association rules mining algorithm vertical union neuroevolution neural networks n/a thema EDItEUR::K Economics, Finance, Business and Management::KN Industry and industrial studies::KNT Media, entertainment, information and communication industries::KNTX Information technology industries |
| url | ONIX_20210501_9783039436118_1137 |