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Genetic Algorithms

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Introduction to Evolutionary Computing

Part of the book series: Natural Computing Series ((NCS))

Abstract

In this chapter we describe the most widely known type of evolutionary algorithm: the genetic algorithm. After presenting a simple example to introduce the basic concepts, we begin with what is usually the most critical decision in any application, namely that of deciding how best to represent a candidate solution to the algorithm. We present four possible solutions, that is, four widely used representations. Following from this we then describe variation operators (mutation and crossover) suitable for different types of representation, before turning our attention to the selection and replacement mechanisms that are used to manage the populations of solutions.

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References

  1. Kenneth De Jong. Genetic algorithms are NOT function optimizers. In Whitley [420], pages 5–18. [99]

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  2. D.E. Goldberg. Genetic Algorithms in Search, Optimization and Machine Learning. Addison-Wesley, 1989. [172] A classic book that had a great impact in promoting the field. On page 6, Figure 1.4 it suggests that GAs are robust methods working well across a broad spectrum of problems.

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  3. J.H. Holland. Adaption in natural and artificial systems. MIT Press, 1992. [204] First edition: 1975, The University of Michigan.

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  4. Z. Michalewicz. Genetic Algorithms + Data structures — Evolution programs. Springer, Berlin, 3rd edition, 1996. [271] This book put the EC field into a new perspective, emphasising the usefulness of problem specific heuristic knowledge within an EA. Compare Figure 14.3 on page 293 with Goldberg’s view.

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  5. M. Mitchell. An Introduction to Genetic Algorithms. MIT Press, 1996. [280]

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Author information

Authors and Affiliations

  1. Faculty of Sciences, Vrije Universiteit Amsterdam, De Boelelaan 1081a, 1081 HV, Amsterdam, The Netherlands

    Prof. Dr. A. E. Eiben

  2. Faculty of Computing, Engineering and Mathematical Sciences, University of the West of England, BS16 1QY, Bristol, UK

    Dr. J. E. Smith

Authors
  1. Prof. Dr. A. E. Eiben
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  2. Dr. J. E. Smith
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© 2003 Springer-Verlag Berlin Heidelberg

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Eiben, A.E., Smith, J.E. (2003). Genetic Algorithms. In: Introduction to Evolutionary Computing. Natural Computing Series. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-05094-1_3

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  • DOI: https://doi.org/10.1007/978-3-662-05094-1_3

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-07285-7

  • Online ISBN: 978-3-662-05094-1

  • eBook Packages: Springer Book Archive

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