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Genetic Algorithms

In: Search Methodologies

Author

Listed:
  • Kumara Sastry

    (University of Illinois)

  • David E. Goldberg

    (Inc. and University of Illinois)

  • Graham Kendall

    (University of Nottingham
    University of Nottingham)

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.

Suggested Citation

  • Kumara Sastry & David E. Goldberg & Graham Kendall, 2014. "Genetic Algorithms," Springer Books, in: Edmund K. Burke & Graham Kendall (ed.), Search Methodologies, edition 2, chapter 0, pages 93-117, Springer.
    • Handle: RePEc:spr:sprchp:978-1-4614-6940-7_4
    DOI: 10.1007/978-1-4614-6940-7_4
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