The evolutionary approach for gaming is different from the traditional one that exploits knowledge of the opening, middle, and endgame stages. It is, therefore, sometimes inefficient to evolve simple heuristics that may be created easily by humans because it is based purely on a bottom-up style of construction. Incorporating domain knowledge into evolutionary computation can improve the performance of evolved strategies and accelerate the speed of evolution by reducing the search space. In this paper, we propose the systematic insertion of opening knowledge and an endgame database into the framework of evolutionary checkers. Also, the common knowledge that the combination of diverse strategies is better than a single best one is included in the middle stage and is implemented using crowding algorithm and a strategy combination scheme. Experimental results show that the proposed method is promising for generating better strategies.
Bibliographical noteFunding Information:
Manuscript received August 29, 2004; revised January 17, 2005 and May 22, 2005. This work was supported in part by the Brain Science and Engineering Research Program sponsored by Korean Ministry of Commerce, Industry, and Energy. The authors are with the Department of Computer Science, Yonsei University, Seoul 120-749, Korea (e-mail: firstname.lastname@example.org; email@example.com). Digital Object Identifier 10.1109/TEVC.2005.856213
All Science Journal Classification (ASJC) codes
- Theoretical Computer Science
- Computational Theory and Mathematics