Optimization with Genetic Algorithms in Multi-Species Environments

Authors:
Lothar M. Schmitt
Affiliations:
-
Venue:
ICCIMA '03 Proceedings of the 5th International Conference on Computational Intelligence and Multimedia Applications
Year:
2003

Citing 0
Cited 2

Theory of genetic algorithms II: models for genetic operators over the string-tensor representation of populations and convergence to global optima for arbitrary fitness function under scaling

Theoretical Computer Science
Theory of coevolutionary genetic algorithms

ISPA'03 Proceedings of the 2003 international conference on Parallel and distributed processing and applications

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Abstract

We discuss a converging "scaled coevolutionary genetic algorithm" (scGA) in a setting where populations contain fixed numbers of interacting creatures of several types. The interaction defines a population-dependent fitness function. The scGA employs multiple-spot mutation, various crossover operators and power-law scaled proportional fitness selection. In particular, the Vose-Liepins version of mutation-crossover is included. To achieve convergence, the mutation and crossover rates have to be annealed to zero in proper fashion, and power-law scaling is used with logarithmic growth in the exponent. If creatures of specific types exist that have maximal fitness in every population they reside in, then the scGA described here converges asymptotically to a probability distribution over multi-uniform populations containing only such maximal creatures wherever they exist.