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Multimodal Optimization by Means of Evolutionary Algorithms
This book offers the first comprehensive taxonomy for multimodal optimization algorithms, work with its root in topics such as niching, parallel evolutionary algorithms, and global optimization. The author explains niching in evolutionary algorithms and its benefits; he examines their suitability fo...
| Clasificación: | Libro Electrónico |
|---|---|
| Autor principal: | |
| Autor Corporativo: | |
| Formato: | Electrónico eBook |
| Idioma: | Inglés |
| Publicado: |
Cham :
Springer International Publishing : Imprint: Springer,
2015.
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| Edición: | 1st ed. 2015. |
| Colección: | Natural Computing Series,
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| Temas: | |
| Acceso en línea: | Texto Completo |
MARC
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| 100 | 1 | |a Preuss, Mike. |e author. |4 aut |4 http://id.loc.gov/vocabulary/relators/aut | |
| 245 | 1 | 0 | |a Multimodal Optimization by Means of Evolutionary Algorithms |h [electronic resource] / |c by Mike Preuss. |
| 250 | |a 1st ed. 2015. | ||
| 264 | 1 | |a Cham : |b Springer International Publishing : |b Imprint: Springer, |c 2015. | |
| 300 | |a XX, 189 p. 42 illus., 5 illus. in color. |b online resource. | ||
| 336 | |a text |b txt |2 rdacontent | ||
| 337 | |a computer |b c |2 rdamedia | ||
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| 490 | 1 | |a Natural Computing Series, |x 2627-6461 | |
| 505 | 0 | |a Introduction: Towards Multimodal Optimization -- Experimentation in Evolutionary Computation -- Groundwork for Niching -- Nearest-Better Clustering -- Niching Methods and Multimodal Optimization Performance -- Nearest-Better Based Niching. | |
| 520 | |a This book offers the first comprehensive taxonomy for multimodal optimization algorithms, work with its root in topics such as niching, parallel evolutionary algorithms, and global optimization. The author explains niching in evolutionary algorithms and its benefits; he examines their suitability for use as diagnostic tools for experimental analysis, especially for detecting problem (type) properties; and he measures and compares the performances of niching and canonical EAs using different benchmark test problem sets. His work consolidates the recent successes in this domain, presenting and explaining use cases, algorithms, and performance measures, with a focus throughout on the goals of the optimization processes and a deep understanding of the algorithms used. The book will be useful for researchers and practitioners in the area of computational intelligence, particularly those engaged with heuristic search, multimodal optimization, evolutionary computing, and experimental analysis. | ||
| 650 | 0 | |a Algorithms. | |
| 650 | 0 | |a Computational intelligence. | |
| 650 | 0 | |a Mathematical optimization. | |
| 650 | 1 | 4 | |a Algorithms. |
| 650 | 2 | 4 | |a Computational Intelligence. |
| 650 | 2 | 4 | |a Optimization. |
| 710 | 2 | |a SpringerLink (Online service) | |
| 773 | 0 | |t Springer Nature eBook | |
| 776 | 0 | 8 | |i Printed edition: |z 9783319074061 |
| 776 | 0 | 8 | |i Printed edition: |z 9783319074085 |
| 776 | 0 | 8 | |i Printed edition: |z 9783319791562 |
| 830 | 0 | |a Natural Computing Series, |x 2627-6461 | |
| 856 | 4 | 0 | |u https://doi.uam.elogim.com/10.1007/978-3-319-07407-8 |z Texto Completo |
| 912 | |a ZDB-2-SCS | ||
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| 950 | |a Computer Science (SpringerNature-11645) | ||
| 950 | |a Computer Science (R0) (SpringerNature-43710) | ||