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Multimodal Optimization by Means of Evolutionary Algorithms

  • Mike Preuss

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

Introduction

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.

Keywords

Evolutionary algorithms Evolutionary computing Experimental analysis Multimodal optimization Niching Optimization

Authors and affiliations

  • Mike Preuss
    • 1
  1. 1.Lehrstuhl für Wirtschaftsinformatik und StatistikWestfälische Wilhelms-Universität MünsterMünsterGermany

Bibliographic information

  • DOI https://doi.org/10.1007/978-3-319-07407-8
  • Copyright Information Springer International Publishing Switzerland 2015
  • Publisher Name Springer, Cham
  • eBook Packages Computer Science
  • Print ISBN 978-3-319-07406-1
  • Online ISBN 978-3-319-07407-8
  • Series Print ISSN 1619-7127
  • Buy this book on publisher's site