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Self-adaptive genetic algorithm learning in game playing

Genetic algorithms (GAs) are known to be effective search methods that are also robust and efficient. We introduce a self-adaptive function for conventional GAs. A dynamic fitness technique helpful for continuous evolution and robust solution is also presented. We expect to improve the quality of GA searches in solving direct competitive problems. We tested our idea by using it to play the game Othello, a typical problem with the direct competitive properties. Experimental results show that our method is better than traditional approaches.