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© 2017

Genetic Algorithm Essentials

Benefits

  • Provides an essential introduction to genetic algorithms (GAs) with an emphasis on making the concepts, algorithms, and applications discussed as easy to understand as possible

  • Presents an overview of strategies for tuning and controlling parameters

  • Includes a brief introduction to theoretical tools for GAs, the intersections and hybridizations with machine learning, and a selection of promising applications

Book

Part of the Studies in Computational Intelligence book series (SCI, volume 679)

Table of contents

  1. Front Matter
    Pages i-ix
  2. Foundations

    1. Front Matter
      Pages 1-1
    2. Oliver Kramer
      Pages 3-10
    3. Oliver Kramer
      Pages 11-19
    4. Oliver Kramer
      Pages 21-28
  3. Solution Spaces

    1. Front Matter
      Pages 29-29
    2. Oliver Kramer
      Pages 31-37
    3. Oliver Kramer
      Pages 39-46
    4. Oliver Kramer
      Pages 47-54
  4. Advanced Concepts

    1. Front Matter
      Pages 55-55
    2. Oliver Kramer
      Pages 57-64
    3. Oliver Kramer
      Pages 65-72
    4. Oliver Kramer
      Pages 73-80
  5. Ending

    1. Front Matter
      Pages 81-81
    2. Oliver Kramer
      Pages 83-84
  6. Back Matter
    Pages 85-92

About this book

Introduction

This book introduces readers to genetic algorithms (GAs) with an emphasis on making the concepts, algorithms, and applications discussed as easy to understand as possible. Further, it avoids a great deal of formalisms and thus opens the subject to a broader audience in comparison to manuscripts overloaded by notations and equations.

The book is divided into three parts, the first of which provides an introduction to GAs, starting with basic concepts like evolutionary operators and continuing with an overview of strategies for tuning and controlling parameters. In turn, the second part focuses on solution space variants like multimodal, constrained, and multi-objective solution spaces. Lastly, the third part briefly introduces theoretical tools for GAs, the intersections and hybridizations with machine learning, and highlights selected promising applications.

Keywords

Introduction to GA Evolutionary Operators Solution Space Variants Computational Intelligence Intelligent Systems Machine Learning

Authors and affiliations

  1. 1.Department für Informatik, Abteilung Computational IntelligenceCarl von Ossietzky Universität OldenburgOldenburgGermany

Bibliographic information

  • Book Title Genetic Algorithm Essentials
  • Authors Oliver Kramer
  • Series Title Studies in Computational Intelligence
  • Series Abbreviated Title Studies Comp.Intelligence
  • DOI https://doi.org/10.1007/978-3-319-52156-5
  • Copyright Information Springer International Publishing AG 2017
  • Publisher Name Springer, Cham
  • eBook Packages Engineering Engineering (R0)
  • Hardcover ISBN 978-3-319-52155-8
  • Softcover ISBN 978-3-319-84834-1
  • eBook ISBN 978-3-319-52156-5
  • Series ISSN 1860-949X
  • Series E-ISSN 1860-9503
  • Edition Number 1
  • Number of Pages IX, 92
  • Number of Illustrations 0 b/w illustrations, 38 illustrations in colour
  • Topics Computational Intelligence
    Artificial Intelligence
  • Buy this book on publisher's site
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