Author

  • Younis Elhaddad

    • University of Benghazi, Benghazi, Libya
    • Libya

Abstract

Genetic algorithm is a well-known heuristic search algorithm, typically used to generate valuable solutions to optimization and search problems. The most important operation in a genetic algorithm is crossover, as it has the greatest effect on its convergence rate. Therefore, in order to achieve the most optimal results in a reasonable time, one has to decide on the crossover type, as well as make a selection of a crossover point. In order to explore the effect of the crossover point selection methods on the convergence rate, we conducted experiments based on different crossover point selection criteria, whereby the results indicate the high importance of controlling the randomization of the crossover point selection range.

Keywords

References

  • Goldberg, D. (1989). Genetic Algorithms in Search, Optimization and Machine Learning, Addison-Wiley Publishing Company, Massachusetts, Mass, USA.
  • http://www.iwr.uni-heidelberg.de/groups/comopt/software/TSPLIB95/. Heidelberg University.
  • Moscato, P. (1989). On genetic crossover operators for relative order preservation, Caltech Concurrent Computation program, Report C3P 778.

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How to Cite

Elhaddad, Y. (2013). An Experimental Study of the Genetic Algorithm Convergence. Journal of Education and Vocational Research, 4(1), 1-4. https://doi.org/10.22610/jevr.v4i1.95

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Copyright (c) 2013 Younis Elhaddad (Author)

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Keywords

Section

Research Paper

Published in

Vol. 4 No. 1 (2013) Algorithms & Digital Learning Access View issue contents

Published

2013-01-30

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