An overview of evolutionary algorithms for parameter optimization

  • Authors:
  • Thomas Bäck;Hans-Paul Schwefel

  • Affiliations:
  • University of Dortmund, Chair of Systems Analysis, P.O. Box 50 05 00, D-4600 Dortmund 50, Germany baeck@lsl1.informatik.uni-dortmund.de;University of Dortmund, Chair of Systems Analysis, P.O. Box 50 05 00, D-4600 Dortmund 50, Germany schwefel@lsl1.informatik.uni-dortmund.de

  • Venue:
  • Evolutionary Computation
  • Year:
  • 1993

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Abstract

Three main streams of evolutionary algorithms (EAs), probabilistic optimization algorithms based on the model of natural evolution, are compared in this article: evolution strategies (ESs), evolutionary programming (EP), and genetic algorithms (GAs). The comparison is performed with respect to certain characteristic components of EAs: the representation scheme of object variables, mutation, recombination, and the selection operator. Furthermore, each algorithm is formulated in a high-level notation as an instance of the general, unifying basic algorithm, and the fundamental theoretical results on the algorithms are presented. Finally, after presenting experimental results for three test functions representing a unimodal and a multimodal case as well as a step function with discontinuities, similarities and differences of the algorithms are elaborated, and some hints to open research questions are sketched.