Advertisement

Understanding EA Dynamics via Population Fitness Distributions

  • Elena Popovici
  • Kenneth De Jong
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 2724)

Abstract

This paper introduces a new tool to be used in conjunction with existing ones for a more comprehensive understanding of the behavior of evolutionary algorithms. Several research groups including [1],[3],[4] have shown how deeper insights into EA behavior can be obtained by focusing on the changes to the entire population fitness distribution rather than just ”best-so-far” curves. But characterizing how repeated applications of selection and reproduction modify this distribution over time proved to be very difficult to achieve analytically and was done successfully for only a few very specialized EAs and/or very simple fitness landscapes.

References

  1. 1.
    Lee Altenberg. The Schema Theorem and Price’s Theorem. In L. Darrell Whitley and Michael D. Vose, editors, Foundations of Genetic Algorithms 3, pages 23–49, Estes Park, Colorado, USA, 1995. Morgan Kaufmann.Google Scholar
  2. 2.
    R. Jain. The Art of Computer Systems Performance Analysis. John Wiley and Sons, Inc., New York, 1991.zbMATHGoogle Scholar
  3. 3.
    H. Mühlenbein and D. Schlierkamp-Voosen. Predictive models for the breeder genetic algorithm. Evolutionary Computation, 1(1):25–49, 1993.CrossRefGoogle Scholar
  4. 4.
    J. Shapiro, A. Prügel-Bennett, and M. Rattray. A statistical mechanical formulation of the dynamics of genetic algorithms. In Terence C. Fogarty, editor, Evolutionary Computing, AISB Workshop, volume 993 of Lecture Notes in Computer Science. Springer, 1994.Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2003

Authors and Affiliations

  • Elena Popovici
    • 1
  • Kenneth De Jong
    • 1
  1. 1.Department of Computer ScienceGeorge Mason UniversityFairfax

Personalised recommendations