Nature-inspired Methods for Stochastic, Robust and Dynamic Optimization
Nature-inspired algorithms have a great popularity in the current scientific community, being the focused scope of many research contributions in the literature year by year. The rationale behind the acquired momentum by this broad family of methods lies on their outstanding performance evinced in h...
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| Format: | Online |
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| Idioma: | anglès |
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IntechOpen
2021
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| Accés en línia: | ONIX_20210420_9781789233292_2290 |
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| _version_ | 1869529467791081472 |
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| collection | Directory of Open Access Books |
| description | Nature-inspired algorithms have a great popularity in the current scientific community, being the focused scope of many research contributions in the literature year by year. The rationale behind the acquired momentum by this broad family of methods lies on their outstanding performance evinced in hundreds of research fields and problem instances. This book gravitates on the development of nature-inspired methods and their application to stochastic, dynamic and robust optimization. Topics covered by this book include the design and development of evolutionary algorithms, bio-inspired metaheuristics, or memetic methods, with empirical, innovative findings when used in different subfields of mathematical optimization, such as stochastic, dynamic, multimodal and robust optimization, as well as noisy optimization and dynamic and constraint satisfaction problems. |
| format | Online |
| id | doab-20.500.12854ir-66931 |
| institution | Directory of Open Access Books |
| language | eng |
| publishDate | 2021 |
| publishDateRange | 2021 |
| publishDateSort | 2021 |
| publisher | IntechOpen |
| publisherStr | IntechOpen |
| record_format | ojs |
| spelling | doab-20.500.12854ir-669312024-04-04T14:41:14Z Nature-inspired Methods for Stochastic, Robust and Dynamic Optimization Del Ser, Javier Osaba, Eneko Optimization thema EDItEUR::P Mathematics and Science::PB Mathematics::PBT Probability and statistics Nature-inspired algorithms have a great popularity in the current scientific community, being the focused scope of many research contributions in the literature year by year. The rationale behind the acquired momentum by this broad family of methods lies on their outstanding performance evinced in hundreds of research fields and problem instances. This book gravitates on the development of nature-inspired methods and their application to stochastic, dynamic and robust optimization. Topics covered by this book include the design and development of evolutionary algorithms, bio-inspired metaheuristics, or memetic methods, with empirical, innovative findings when used in different subfields of mathematical optimization, such as stochastic, dynamic, multimodal and robust optimization, as well as noisy optimization and dynamic and constraint satisfaction problems. 2021-04-20T15:57:47Z 2021-04-20T15:57:47Z 2018 book ONIX_20210420_9781789233292_2290 9781789233292 9781789233285 9781838815721 https://directory.doabooks.org/handle/20.500.12854/66931 eng image/jpeg n/a https://www.intechopen.com/books https://mts.intechopen.com/storage/books/6587/authors_book/authors_book.pdf IntechOpen IntechOpen 10.5772/intechopen.71401 10.5772/intechopen.71401 78a36484-2c0c-47cb-ad67-2b9f5cd4a8f6 9781789233292 9781789233285 9781838815721 IntechOpen 70 open access |
| spellingShingle | Optimization thema EDItEUR::P Mathematics and Science::PB Mathematics::PBT Probability and statistics Nature-inspired Methods for Stochastic, Robust and Dynamic Optimization |
| title | Nature-inspired Methods for Stochastic, Robust and Dynamic Optimization |
| title_full | Nature-inspired Methods for Stochastic, Robust and Dynamic Optimization |
| title_fullStr | Nature-inspired Methods for Stochastic, Robust and Dynamic Optimization |
| title_full_unstemmed | Nature-inspired Methods for Stochastic, Robust and Dynamic Optimization |
| title_short | Nature-inspired Methods for Stochastic, Robust and Dynamic Optimization |
| title_sort | nature inspired methods for stochastic robust and dynamic optimization |
| topic | Optimization thema EDItEUR::P Mathematics and Science::PB Mathematics::PBT Probability and statistics |
| topic_facet | Optimization thema EDItEUR::P Mathematics and Science::PB Mathematics::PBT Probability and statistics |
| url | ONIX_20210420_9781789233292_2290 |