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Genetic Algorithm Essentials

  • Book
  • © 2017

Overview

Authors:
  1. Oliver Kramer

    1. Department für Informatik, Abteilung Computational Intelligence, Carl von Ossietzky Universität Oldenburg, Oldenburg, Germany

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  • 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
  • Includes supplementary material: sn.pub/extras

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

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Table of contents (10 chapters)

  1. Front Matter

    Pages i-ix
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  2. Foundations

    1. Front Matter

      Pages 1-1
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    2. Introduction

      • Oliver Kramer
      Pages 3-10

    3. Genetic Algorithms

      • Oliver Kramer
      Pages 11-19

    4. Parameters

      • Oliver Kramer
      Pages 21-28

  3. Solution Spaces

    1. Front Matter

      Pages 29-29
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    2. Multimodality

      • Oliver Kramer
      Pages 31-37

    3. Constraints

      • Oliver Kramer
      Pages 39-46

    4. Multiple Objectives

      • Oliver Kramer
      Pages 47-54

  4. Advanced Concepts

    1. Front Matter

      Pages 55-55
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    2. Theory

      • Oliver Kramer
      Pages 57-64

    3. Machine Learning

      • Oliver Kramer
      Pages 65-72

    4. Applications

      • Oliver Kramer
      Pages 73-80

  5. Ending

    1. Front Matter

      Pages 81-81
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    2. Summary and Outlook

      • Oliver Kramer
      Pages 83-84

  6. Back Matter

    Pages 85-92
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Keywords

About this book

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.


Authors and Affiliations

  • Department für Informatik, Abteilung Computational Intelligence, Carl von Ossietzky Universität Oldenburg, Oldenburg, Germany

    Oliver Kramer

Bibliographic Information

  • Book Title: Genetic Algorithm Essentials

  • Authors: Oliver Kramer

  • Series Title: Studies in Computational Intelligence

  • DOI: https://doi.org/10.1007/978-3-319-52156-5

  • Publisher: Springer Cham

  • eBook Packages: Engineering, Engineering (R0)

  • Copyright Information: Springer International Publishing AG, part of Springer Nature 2017

  • Hardcover ISBN: 978-3-319-52155-8Published: 13 January 2017

  • Softcover ISBN: 978-3-319-84834-1Published: 13 July 2018

  • eBook ISBN: 978-3-319-52156-5Published: 07 January 2017

  • Series ISSN: 1860-949X

  • Series E-ISSN: 1860-9503

  • Edition Number: 1

  • Number of Pages: IX, 92

  • Number of Illustrations: 38 illustrations in colour

  • Topics: Computational Intelligence, Artificial Intelligence

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