An Analysis of the Interacting Roles of Population Size and Crossover in Genetic Algorithms   [PS] [GA]

by

Jong, K., A., D. and Spears, W., M.

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Info: Parallel Problem Solving from Nature (Conference proceedings), 1991, p. 38-47
Abstract:
In this paper we present some theoretical and empirical results on the interacting roles of population size [PS] and crossover in genetic algorithms. [GA] We summarize recent theoretical results on the disruptive effect of two forms of multi-point crossover: n-point crossover and uniform crossover. [UC] We then show empirically that disruption analysis alone is not sufficient for selecting appropriate forms of crossover. However, by taking into account the interacting effects of population size [PS] and crossover, a general picture begins to emerge. The implications of these results on implementation issues and performance are discussed, and several directions for further research are suggested.
Notes:
Conference citation: First International Conference on Parallel Problem Solving from Nature, October 1-3, 1990, Dortmund, Germany, IEEE Society Press
Author(s) DL:Online papers for Spears, W., M.
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BibTex:
@INPROCEEDINGS{DeJong1991,
CROSSREF = {Schwefel1991},
AUTHOR   = {Kenneth A. De Jong and Wiiliam M. Spears},
TITLE    = {An Analysis of the Interacting Roles of Population Size
            and Crossover in Genetic Algorithms},
PAGES    = {38--47},
  other =        "Conference citation: First International Conference on
                 Parallel Problem Solving from Nature, October 1-3,
                 1990, Dortmund, Germany, IEEE Society Press",
  number =       "AIC-90-003",
  abstract =     "In this paper we present some theoretical and
                 empirical results on the interacting roles of
                 population size and crossover in genetic algorithms. We
                 summarize recent theoretical results on the disruptive
                 effect of two forms of multi-point crossover: n-point
                 crossover and uniform crossover. We then show
                 empirically that disruption analysis alone is not
                 sufficient for selecting appropriate forms of
                 crossover. However, by taking into account the
                 interacting effects of population size and crossover, a
                 general picture begins to emerge. The implications of
                 these results on implementation issues and performance
                 are discussed, and several directions for further
                 research are suggested.",
  institution =  "Navy Center for Applied Research in Artificial
                 Intelligence",
}