Multimodal Optimization by Means of Evolutionary Algorithms (Natural Computing Series)

Preuss, Mike

ISBN 10: 3319791567 ISBN 13: 9783319791562
Verlag: Springer, 2019
Neu Softcover

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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.

Über die Autorin bzw. den Autor:

Dr. Mike Preuss got his Ph.D. in the Technische Universität Dortmund and he is now a researcher at the Westfälische Wilhelms-Universität Münster. He has published in the leading journals and conferences on various aspects of computational intelligence, in particular evolutionary computing, heuristics, search and multicriteria optimization and served on many of the key academic conference committees, journal boards and review committees in this field. He is a leading figure in the application of computational and artificial intelligence to games.

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Titel: Multimodal Optimization by Means of ...
Verlag: Springer
Erscheinungsdatum: 2019
Einband: Softcover
Zustand: New

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Mike Preuss
ISBN 10: 3319791567 ISBN 13: 9783319791562
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Taschenbuch. Zustand: Neu. Druck auf Anfrage Neuware - Printed after ordering - 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. Artikel-Nr. 9783319791562

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Preuss, Mike
ISBN 10: 3319791567 ISBN 13: 9783319791562
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Paperback. Zustand: Brand New. reprint edition. 212 pages. 9.25x6.10x0.48 inches. In Stock. Artikel-Nr. x-3319791567

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