Nature-inspired methods for stochastic, robust and dynamic optimization / / Javier Del Ser, Eneko Osaba, editors.
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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| Place / Publishing House: | [Place of publication not identified] : : IntechOpen,, [2018] ©2018 |
| Year of Publication: | 2018 |
| Language: | English |
| Physical Description: | 1 online resource (70 pages) :; illustrations |
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| Summary: | 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. |
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| Bibliography: | Includes bibliographical references. |
| ISBN: | 1838815724 1789233291 |
| Hierarchical level: | Monograph |
| Statement of Responsibility: | Javier Del Ser, Eneko Osaba, editors. |