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Evolutionary consequences of coevolving targets

Pagie1, Hogeweg

  • 1Utrecht University, Department of Bioinformatics, The Netherlands. pagie@encode.biol.ruu.nl

Evolutionary Computation
|January 1, 1997
PubMed
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Evolutionary algorithms perform better with sparse, coevolving fitness evaluations rather than static ones. Sparse evaluation leads to more generalizable and less mutationally stable evolved programs.

Area of Science:

  • Evolutionary Computation
  • Artificial Intelligence
  • Computer Science

Background:

  • Traditional evolutionary optimization relies on static fitness evaluations.
  • Natural evolution involves dynamic and coevolving environments, not static problem sets.
  • Understanding information integration across generations is key.

Purpose of the Study:

  • To investigate the impact of different fitness evaluation schemes on evolved genotypes and phenotypes.
  • To compare static versus sparse, coevolving fitness evaluations in genetic programming.

Main Methods:

  • Utilized genetic programming with a functional representation for evolving programs.
  • Compared fitness evaluation using a large static problem set versus small coevolving problem sets.

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  • Analyzed the resulting genotypes and phenotypes for correctness, generalizability, and mutational stability.

    • Main Results:

    • Sparse, coevolving fitness evaluation successfully produced correct solutions in about half of simulations.
    • Complete static fitness evaluation failed to find correct solutions in any simulation.
    • Programs evolved under sparse evaluation demonstrated superior generalizability on denser problem sets.
    • Sparse evaluation resulted in programs with lower mutational stability compared to static evaluation.

    Conclusions:

    • Sparse, coevolving fitness evaluation schemes can be more effective than static ones for evolutionary optimization.
    • Evolved programs under sparse evaluation exhibit better generalization capabilities.
    • The trade-off between generalization and mutational stability is influenced by the fitness evaluation scheme.