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Theoretical and Experimental Study of Crossover Operators of Genetic Algorithms

Author

Listed:
  • N. P. Belfiore

    (University of Rome)

  • A. Esposito

    (University of Rome)

Abstract

This paper is concerned with crossover operators for genetic algorithms (GAs) which are used to solve problems based on real numbers. First, a classification of the operators is introduced, dividing crossover into a vector-level and a variable-level operator. The theoretical study of variable-level operators for binary coded GAs leads to the discovery of two properties, which are used to define certain characteristics of crossover operators used by real-number encoded GAs. For variable-level operators, the experimental distributions of the offspring variables of given pairs of parent variables are then found. Finally, an experimental comparison of crossover operator performance is carried out.

Suggested Citation

  • N. P. Belfiore & A. Esposito, 1998. "Theoretical and Experimental Study of Crossover Operators of Genetic Algorithms," Journal of Optimization Theory and Applications, Springer, vol. 99(2), pages 271-302, November.
    • Handle: RePEc:spr:joptap:v:99:y:1998:i:2:d:10.1023_a:1021766025497
    DOI: 10.1023/A:1021766025497
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    Cited by:

    1. Haldun Aytug & Gary J. Koehler & Ling He, 2008. "Risk Minimization and Minimum Description for Linear Discriminant Functions," INFORMS Journal on Computing, INFORMS, vol. 20(2), pages 317-331, May.

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