Methods for Competitive Co-evolution: Finding Opponents Worth Beating (1995)
| Venue: | Proceedings of the Sixth International Conference on Genetic Algorithms |
| Citations: | 90 - 2 self |
BibTeX
@INPROCEEDINGS{Rosin95methodsfor,
author = {Christopher D. Rosin and Richard K. Belew},
title = {Methods for Competitive Co-evolution: Finding Opponents Worth Beating},
booktitle = {Proceedings of the Sixth International Conference on Genetic Algorithms},
year = {1995},
pages = {373--380},
publisher = {Morgan Kaufmann}
}
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Abstract
Co-evolution refers to the simultaneous evolution of two or more genetically distinct populations with coupled fitness landscapes. In this paper we consider "competitive co-evolution," in which the fitness of an individual in a "host" population is based on direct competition with individual(s) from a "parasite" population. Competitive coevolution is applied to three game-learning problems: Tic-Tac-Toe (TTT), Nim and a small version of Go. Two new techniques in competitive co-evolution are explored. "Competitive fitness sharing" changes the way fitness is measured, and "shared sampling" alters the way parasites are chosen for testing hosts. Experiments using TTT and Nim show a substantial improvement in performance when these methods are used. Preliminary results using co-evolution for the discovery of cellular automata rules for playing Go are presented. 1 Introduction Co-evolution refers to the simultaneous evolution of two or more genetically distinct populations with coupled fit...
