Genetic algorithm learning in game playing with multiple coaches

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)