Elsevier

Computers & Chemistry

Volume 18, Issue 2, June 1994, Pages 137-156
Computers & Chemistry

Gates towards evolutionary large-scale optimization: A software-oriented approach to genetic algorithms—II. Toolbox description

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Abstract

Starting from Part I, Part II of this paper globally describes the toolbox GATES, elucidating how concepts of genetic algorithm methodology can be implemented for application. The prototype for numerical parameter estimation is treated in most detail. Additionally, an auxiliary utility for configuration, based on explorative walks in the search space, is outlined.

References (38)

  • L. Davis

    Applying adaptive algorithms to epistatic domains

    Ninth International Joint Conference on Artificial Intelligence

    (1985)
  • L. Davis
    (1991)
  • Jong K.A. De

    An analysis of the Behavior of a Class of Genetic Adaptive Systems

    (1975)
  • L.J. Eshelman et al.

    Biases in the crossover landscape

  • E. Falkenauer
    (1991)
  • E. Falkenauer

    A genetic algorithm for grouping

  • D.E. Goldberg et al.

    Alleles, loci, and the traveling salesman problem

  • D.E. Goldberg
    (1989)
  • J.H. Holland
    (1975)
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