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Design of Representations and Search Operators

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Springer Handbook of Computational Intelligence

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Abstract

Successful and efficient use of evolutionary algorithms depends on the choice of genotypes and the representation – that is, the mapping from genotype to phenotype – and on the choice of search operators that are applied to the genotypes. These choices cannot be made independently of each other. This chapter gives recommendations on the design of representations and corresponding search operators and discusses how to consider problem-specific knowledge. For most problems in the real world, similar solutions have similar fitness values. This fact can be exploited by evolutionary algorithms if they ensure that the representations and search operators used are defined in such a way that similarities between phenotypes correspond to similarities between genotypes. Furthermore, the performance of evolutionary algorithms can be increased by problem-specific knowledge. We discuss how properties of high-quality solutions can be exploited by biasing representations and search operators.

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Abbreviations

ARGOT:

adaptive representation genetic optimization technique

DNA:

deoxyribonucleic acid

EA:

evolutionary algorithm

EDA:

estimation of distribution algorithm

HSS:

heuristic space search

NP:

nondeterministic polynomial-time

PSS:

problem space search

TSP:

traveling salesman problem

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  1. Gutenberg School of Management and Economics, Johannes Gutenberg University Mainz, Jakob Welder-Weg 9, 55099, Mainz, Germany

    Franz Rothlauf

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  1. Systems Research Inst., Polish Academy of Sciences, ul. Newelska 6, 01-447, Warsaw, Poland

    Janusz Kacprzyk

  2. Dep. Electrical and Computer Engineering, University of Alberta, 116 Street 9107, T6J 2V4, Edmonton, Alberta, Canada

    Witold Pedrycz

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Rothlauf, F. (2015). Design of Representations and Search Operators. In: Kacprzyk, J., Pedrycz, W. (eds) Springer Handbook of Computational Intelligence. Springer Handbooks. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-43505-2_53

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  • DOI: https://doi.org/10.1007/978-3-662-43505-2_53

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-662-43504-5

  • Online ISBN: 978-3-662-43505-2

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