Multimodal Optimization by Means of Evolutionary Algorithms
Details
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.
Describes state of the art in algorithms, measures and test problems Approaches multimodal optimization algorithms via model-based simulation and statistics Valuable for practitioners with real-world black-box problems
Autorentext
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.
Inhalt
Introduction: Towards Multimodal Optimization.- Experimentation in Evolutionary Computation.- Groundwork for Niching.- Nearest-Better Clustering.- Niching Methods and Multimodal Optimization Performance.- Nearest-Better Based Niching.
Weitere Informationen
- Allgemeine Informationen
- GTIN 09783319791562
- Sprache Englisch
- Auflage Softcover reprint of the original 1st edition 2015
- Größe H235mm x B155mm x T12mm
- Jahr 2019
- EAN 9783319791562
- Format Kartonierter Einband
- ISBN 3319791567
- Veröffentlichung 14.03.2019
- Titel Multimodal Optimization by Means of Evolutionary Algorithms
- Autor Mike Preuss
- Untertitel Natural Computing Series
- Gewicht 330g
- Herausgeber Springer International Publishing
- Anzahl Seiten 212
- Lesemotiv Verstehen
- Genre Mathematik