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Heuristic crossovers for real-coded genetic algorithms based on fuzzy connectives

  • Modifications and Extensions of Evolutionary Algorithms Genetic Operators and Problem Representation
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Parallel Problem Solving from Nature — PPSN IV (PPSN 1996)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 1141))

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Abstract

A problem in the use of genetic algorithms is the premature convergence in a local optimum. Its main cause is the lack of diversity in the population due to a disproportionate relationship between exploitation and exploration. In this paper, we present heuristic crossover operators for real-coded genetic algorithms, which use the adaptation of the parents for generating the offspring. With these operators, diversity and convergence in the population may be modeled in order to avoid the premature convergence problem and to introduce good final behaviour.

This research has been supported by DGICYT PB92-0933.

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Authors and Affiliations

  1. Department of Computer Science and Artificial Intelligence, University of Granada, 18071, Granada, Spain

    Francisco Herrera & Manuel Lozano

Authors
  1. Francisco Herrera
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  2. Manuel Lozano
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Editor information

Hans-Michael Voigt Werner Ebeling Ingo Rechenberg Hans-Paul Schwefel

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© 1996 Springer-Verlag Berlin Heidelberg

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Herrera, F., Lozano, M. (1996). Heuristic crossovers for real-coded genetic algorithms based on fuzzy connectives. In: Voigt, HM., Ebeling, W., Rechenberg, I., Schwefel, HP. (eds) Parallel Problem Solving from Nature — PPSN IV. PPSN 1996. Lecture Notes in Computer Science, vol 1141. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-61723-X_998

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  • DOI: https://doi.org/10.1007/3-540-61723-X_998

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-61723-5

  • Online ISBN: 978-3-540-70668-7

  • eBook Packages: Springer Book Archive

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