Evolving speciated checkers players with crowding algorithm
Kyung-Joong Kim, Sung-Bae Cho · 2003
Conventional evolutionary algorithms have a property that only one solution often dominates and it is sometimes useful to find diverse solutions and combine them because there might be many different solutions to one problem in real-world problems. Recently, developing checkers players using evolutionary algorithms has been widely exploited to show the power of evolution for machine learning. In this paper, we propose an evolutionary checkers player that is developed by a speciation technique called the "crowding algorithm". In many experiments, our checkers player with an ensemble structure showed better performance than non-speciated checkers players. A neural network is used to validate the game board, and a min-max search finds the optimal board. The neural network evaluator is evolved using the evolutionary algorithm.