© 2015

Multimodal Optimization by Means of Evolutionary Algorithms


  • 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


Part of the Natural Computing Series book series (NCS)

Table of contents

  1. Front Matter
    Pages i-xx
  2. Mike Preuss
    Pages 55-73
  3. Mike Preuss
    Pages 139-170
  4. Mike Preuss
    Pages 171-175
  5. Back Matter
    Pages 177-189

About this book


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.


Evolutionary algorithms Evolutionary computing Experimental analysis Multimodal optimization Niching Optimization

Authors and affiliations

  1. 1.Lehrstuhl für Wirtschaftsinformatik und StatistikWestfälische Wilhelms-Universität MünsterMünsterGermany

About the authors

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.

Bibliographic information

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“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)