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Genetic Algorithms

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Search Methodologies

Abstract

Genetic algorithms (GAs) are search methods based on principles of natural selection and genetics (Fraser 1957; Bremermann 1958; Holland 1975). We start with a brief introduction of simple GAs and the associated terminologies. GAs encode the decision variables of a search problem into finite-length strings of alphabets of certain cardinality. The strings which are candidate solutions to the search problem are referred to as chromosomes, the alphabets are referred to as genes and the values of genes are called alleles. For example, in a problem such as the traveling salesman problem (TSP), a chromosome represents a route, and a gene may represent a city. In contrast to traditional optimization techniques, GAs work with coding of parameters, rather than the parameters themselves.

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Author notes

  1. Kumara Sastry

    Present address: University of Illinois, Urbana-Champaign, IL, USA

Authors and Affiliations

  1. Inc. and University of Illinois, Urbana-Champaign, IL, USA

    David E. Goldberg (ThreeJoy Associates)

  2. Automated Scheduling, Optimization and Planning Research Group, School of Computer Science, University of Nottingham, Nottingham, UK

    Graham Kendall

  3. Automated Scheduling, Optimization and Planning Research Group, School of Computer Science, University of Nottingham, Semenyih, Malaysia

    Graham Kendall

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  1. Kumara Sastry
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  2. David E. Goldberg
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  3. Graham Kendall
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Correspondence to Graham Kendall .

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Editors and Affiliations

  1. Computing Science and Mathematics, University of Stirling, Scotland, United Kingdom

    Edmund K. Burke

  2. Research and Knowledge Transfer, University of Nottingham, Malaysia & UK, Semenyih, Malaysia

    Graham Kendall

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Sastry, K., Goldberg, D.E., Kendall, G. (2014). Genetic Algorithms. In: Burke, E., Kendall, G. (eds) Search Methodologies. Springer, Boston, MA. https://doi.org/10.1007/978-1-4614-6940-7_4

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