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OverviewThis 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. Full Product DetailsAuthor: Mike PreussPublisher: Springer International Publishing AG Imprint: Springer International Publishing AG Edition: Softcover reprint of the original 1st ed. 2015 Weight: 0.454kg ISBN: 9783319791562ISBN 10: 3319791567 Pages: 189 Publication Date: 14 March 2019 Audience: Professional and scholarly , Professional & Vocational Format: Paperback Publisher's Status: Active Availability: Manufactured on demand  We will order this item for you from a manufactured on demand supplier. Table of ContentsIntroduction: Towards Multimodal Optimization.- Experimentation in Evolutionary Computation.- Groundwork for Niching.- Nearest-Better Clustering.- Niching Methods and Multimodal Optimization Performance.- Nearest-Better Based Niching.ReviewsIt provides an excellent explanation of the theoretical background of many topics in evolutionary computation ... . I strongly recommend this book for graduate students or any researcher who wants to work in the EC field ... . It also may help in improving some algorithms and may motivate the researcher to introduce new ones. ... the chapters are self-contained so that you can read individual chapters that you are interested in without the need to read the whole book. (Nailah Al-Madi, Genetic Programming and Evolvable Machines, Vol. 17 (3), September, 2016) “It provides an excellent explanation of the theoretical background of many topics in evolutionary computation … . I strongly recommend this book for graduate students or any researcher who wants to work in the EC field … . It also may help in improving some algorithms and may motivate the researcher to introduce new ones. … the chapters are self-contained so that you can read individual chapters that you are interested in without the need to read the whole book.” (Nailah Al-Madi, Genetic Programming and Evolvable Machines, Vol. 17 (3), September, 2016) Author InformationDr. 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. Tab Content 6Author Website:Countries AvailableAll regions |
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