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Genetic algorithm learning in game playing with multiple coaches

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4 Author(s)
Sun, C.-T. ; Dept. of Comput. & Inf. Sci., Nat. Chiao Tung Univ., Hsinchu, Taiwan ; Ying-Hong Liao ; Jing-Yi Lu ; Fu-May Zheng

Explores the concept of diversified selection by employing multiple coaches in a game-playing program with a genetic algorithm (GA) based learning module. Although the importance of diversity in choosing offspring in a gene pool has been addressed in the past, few authors have discussed how to maintain diversity in real-world applications. Most existing suggestions are based on a balanced distribution of candidates, but this is not a realistic assumption for search problems in a multidimensional space. We show in this paper that when more than one coach is used in a game-playing environment, the collective learning result is better than other learning curves in which only a single coach is involved, no matter whether the coach is the best one or the worst one. We also use expanded chromosomes for measuring position scores in a static evaluation function to achieve improved learnability. Our work can be classified under the evolutionary strategy paradigm mentioned by K. De Jong and W. Spears (1993)

Published in:

Evolutionary Computation, 1994. IEEE World Congress on Computational Intelligence., Proceedings of the First IEEE Conference on

Date of Conference:

27-29 Jun 1994