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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

Summary

This summary is machine-generated.

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.

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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.
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.