Tan, Kar Bin and Teo, Jason Tze Wi and Chin, Kim On and Patricia Anthony, (2012) An evolutionary multi-objective optimization approach to computer Go controller synthesis. In: 12th Pacific Rim International Conference on Artificial Intelligence, PRICAI 2012, 3-7 September 2012, Kuching, Sarawak.
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Official URL: http://dx.doi.org/10.1007/978-3-642-32695-0_76
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.
|Item Type:||Conference Paper (UNSPECIFIED)|
|Uncontrolled Keywords:||Artificial intelligence, Artificial neural networks, Computer go, Evolutionary multi-objective optimization, Single-objective optimization|
|Subjects:||?? QA299.6-433 ??|
|Divisions:||SCHOOL > School of Engineering and Information Technology|
|Deposited By:||IR Admin|
|Deposited On:||30 Oct 2012 17:35|
|Last Modified:||08 Sep 2014 12:42|
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