Genetic Algorithms

  • Kumara Sastry
  • David E. Goldberg
  • Graham Kendall

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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Copyright information

© Springer Science+Business Media New York 2014

Authors and Affiliations

  • Kumara Sastry
    • 1
  • David E. Goldberg
    • 2
  • Graham Kendall
    • 3
    • 4
  1. 1.University of IllinoisUrbana-ChampaignUSA
  2. 2.Inc. and University of IllinoisUrbana-ChampaignUSA
  3. 3.Automated Scheduling, Optimization and Planning Research Group, School of Computer ScienceUniversity of NottinghamNottinghamUK
  4. 4.Automated Scheduling, Optimization and Planning Research Group, School of Computer ScienceUniversity of NottinghamSemenyihMalaysia

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