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International Journal of Computer Games Technology
Volume 2013 (2013), Article ID 170914, 7 pages
Research Article

Single- versus Multiobjective Optimization for Evolution of Neural Controllers in Ms. Pac-Man

Evolutionary Computing Laboratory, School of Engineering and Information Technology, Universiti Malaysia, Jalan (UMS), 88400 Kota Kinabalu, Sabah, Malaysia

Received 19 September 2012; Revised 14 December 2012; Accepted 4 January 2013

Academic Editor: Abdennour El Rhalibi

Copyright © 2013 Tse Guan Tan et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


The objective of this study is to focus on the automatic generation of game artificial intelligence (AI) controllers for Ms. Pac-Man agent by using artificial neural network (ANN) and multiobjective artificial evolution. The Pareto Archived Evolution Strategy (PAES) is used to generate a Pareto optimal set of ANNs that optimize the conflicting objectives of maximizing Ms. Pac-Man scores (screen-capture mode) and minimizing neural network complexity. This proposed algorithm is called Pareto Archived Evolution Strategy Neural Network or PAESNet. Three different architectures of PAESNet were investigated, namely, PAESNet with fixed number of hidden neurons (PAESNet_F), PAESNet with varied number of hidden neurons (PAESNet_V), and the PAESNet with multiobjective techniques (PAESNet_M). A comparison between the single- versus multiobjective optimization is conducted in both training and testing processes. In general, therefore, it seems that PAESNet_F yielded better results in training phase. But the PAESNet_M successfully reduces the runtime operation and complexity of ANN by minimizing the number of hidden neurons needed in hidden layer and also it provides better generalization capability for controlling the game agent in a nondeterministic and dynamic environment.