Journal of Heuristics

, Volume 5, Issue 2, pp 215–247 | Cite as

Schemata, Distributions and Graphical Models in Evolutionary Optimization

  • Heinz Mühlenbein
  • Thilo Mahnig
  • Alberto Ochoa Rodriguez
Article

Abstract

In this paper the optimization of additively decomposed discrete functions is investigated. For these functions genetic algorithms have exhibited a poor performance. First the schema theory of genetic algorithms is reformulated in probability theory terms. A schema defines the structure of a marginal distribution. Then the conceptual algorithm BEDA is introduced. BEDA uses a Boltzmann distribution to generate search points. From BEDA a new algorithm, FDA, is derived. FDA uses a factorization of the distribution. The factorization captures the structure of the given function. The factorization problem is closely connected to the theory of conditional independence graphs. For the test functions considered, the performance of FDA—in number of generations till convergence—is similar to that of a genetic algorithm for the OneMax function. This result is theoretically explained.

graphical models conditional independence graphs additively decomposed functions estimation of distributions population based search genetic algorithm 

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Copyright information

© Kluwer Academic Publishers 1999

Authors and Affiliations

  • Heinz Mühlenbein
    • 1
  • Thilo Mahnig
    • 1
  • Alberto Ochoa Rodriguez
    • 1
  1. 1.Real World Computing Partnership Theoretical Foundation GMD Laboratory, GMD—Forschungszentrum InformationstechnikSt. Augustin

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