Computer Go: an AI oriented survey
Artificial Intelligence
Multi-Objective Optimization in Computational Intelligence: Theory and Practice (Premier Reference Source)
Learning to play Go using recursive neural networks
Neural Networks
Indirect encoding of neural networks for scalable go
PPSN'10 Proceedings of the 11th international conference on Parallel problem solving from nature: Part I
IEEE Transactions on Evolutionary Computation
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Evolutionary multi-objective optimization (EMO) has gained popularity and it has been successfully applied in several research areas. Based on the literature review conducted, EMO approach has not been applied in any Go game application. In this study, artificial neural networks (ANNs) are evolved with an EMO algorithm, Pareto Archived Evolution Strategies (PAES) for computer player to learn and play the 7x7 board Go game against GNU Go. In this study, two conflicting objectives are investigated: first, maximize the ability of neural player to play the Go game and second, minimize the complexity of the ANN by reducing the hidden units. Several comparative empirical experiments were conducted that showed EMO which optimize two distinct and conflicting objectives outperformed the single-objective (SO) optimization which only optimized the first objective with no pressure selection on the second objective.