An evolutionary multi-objective optimization approach to computer go controller synthesis

Abstract

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 abil-ity of neural player to play the Go game and second, minimize the complexity of the ANN by reducing the hidden units. Several comparative empirical exper-iments were conducted that showed EMO which optimize two distinct and con-flicting objectives outperformed the single-objective (SO) optimization which only optimized the first objective with no pressure selection on the second ob-jective.