Multimodal Optimization by Means of Evolutionary Algorithms

ISBN-13: 9783319074061 / Angielski / Twarda / 2015 / 189 str.

Mike Preuss

Opis

The field of research covered by this book is niching/multimodal optimization, with an emphasis on evolutionary computation methods, explaining the state of the art and relating this research to a model-based view of the basin identification problem. The algorithms suggested by the author are compared to alternative recent methods on current benchmarks. This book is useful for researchers and practitioners in the area of evolutionary computing.

Recenzje

"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)

Spis treści

Introduction: Towards Multimodal Optimization.- Experimentation in Evolutionary Computation.- Groundwork for Niching.- Nearest-Better Clustering.- Niching Methods and Multimodal Optimization Performance.- Nearest-Better Based Niching.

O autorze

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.

Blurb

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.