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

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.

Full text not available from this repository.


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 or Workshop Item (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
Depositing User: ADMIN ADMIN
Date Deposited: 30 Oct 2012 09:35
Last Modified: 08 Sep 2014 04:42

Actions (login required)

View Item View Item