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An Adaptive Differential Evolution Algorithm with Opposition-Based Mechanisms, Applied to the Tuning of a Chess Program

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Part of the Studies in Computational Intelligence book series (SCI, volume 143)

Summary

This chapter describes an algorithm for the tuning of a chess program which is based on Differential Evolution using adaptation and opposition based optimization mechanisms. The mutation control parameter F is adapted according to the deviation of search parameters in each generation. Opposition-based optimization is included in the initialization, and in the evolutionary process itself. In order to demonstrate the behaviour of our algorithm we tuned our BBChess chess program with a combination of adaptive and opposition-based optimization. Tuning results show that adaptive optimization with an opposition-based mechanism increases the robustness of the algorithm and has a comparable convergence to the algorithm which uses only adaptation optimization.

Keywords

Differential Evolution Adaptation Tuning of a Chess Program Opposition-Based mechanisms 
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References

  1. 1.
    Baxter, J., Tridgell, A., Weaver, L.: Experiments in Parameter Learning Using Temporal Differences. International Computer Chess Association Journal 21(2), 84–99 (1998)Google Scholar
  2. 2.
    Baxter, J., Tridgell, A., Weaver, L.: Learning to Play Chess Using Temporal Differences. Machine Learning 40(3), 243–263 (2000)zbMATHCrossRefGoogle Scholar
  3. 3.
    Bošković, B., Greiner, S., Brest, J., Žumer, V.: The Representation of Chess Game. In: Proceedings of the 27th International Conference on Information Technology Interfaces, pp. 381–386 (2005)Google Scholar
  4. 4.
    Bošković, B., Greiner, S., Brest, J., Žumer, V.: A Differential Evolution for the Tuning of a Chess Evaluation Function. In: CEC 2006: International Conference on Evolutionary Computation, Vancouver, Canada, July 2006, pp. 6742–6747. IEEE, Los Alamitos (2006)Google Scholar
  5. 5.
    Brest, J., Greiner, S., Bošković, B., Mernik, M., Žumer, V.: Self-Adapting Control Parameters in Differential Evolution: A Comparative Study on Numerical Benchmark Problems. IEEE Transactions on Evolutionary Computation 10(6), 646–657 (2006)CrossRefGoogle Scholar
  6. 6.
    Chisholm, K.: Co-evolving Draughts Strategies with Differential Evolution, ch. 9, pp. 147–158. McGraw-Hill, London (1999)Google Scholar
  7. 7.
    Fogel, D.B., Hays, T.J., Hahn, S.L., Quon, J.: A Self-Learning Evolutionary Chess Program. Proceedings of the IEEE 92(12), 1947–1954 (2004)CrossRefGoogle Scholar
  8. 8.
    Kendall, G., Whitwell, G.: An Evolutionary Approach for the Tuning of a Chess Evaluation Function Using Population Dynamics. In: Proceedings of the 2001 Congress on Evolutionary Computation CEC 2001, COEX, World Trade Center, 159 Samseong-dong, Gangnam-gu, Seoul, Korea, pp. 995–1002. IEEE Press, Los Alamitos (2001)CrossRefGoogle Scholar
  9. 9.
    Price, K., Storn, R.: Differential Evolution: A Simple Evolution Strategy for Fast Optimization. Dr. Dobb’s Journal of Software Tools 22(4), 18–24 (1997)MathSciNetGoogle Scholar
  10. 10.
    Price, K.V., Storn, R.M., Lampinen, J.A.: Differential Evolution, A Practical Approach to Global Optimization. Springer, Heidelberg (2005)zbMATHGoogle Scholar
  11. 11.
    Rahnamayan, S., Tizhoosh, H.R., Salama, M.M.: Opposition-Based Differential Evolution Algorithms. In: CEC 2006: International Conference on Evolutionary Computation, Vancouver, Canada, July 2006, pp. 7363–7370. IEEE, Los Alamitos (2006)Google Scholar
  12. 12.
    Rahnamayan, S., Tizhoosh, H.R., Salama, M.M.A.: Opposition-based differential evolution. IEEE Transactions on Evolutionary Computation 12(1) (2008)Google Scholar
  13. 13.
    Samuel, A.L.: Some studies in machine learning using the game of checkers. IBM Journal of Research and Development (3), 211–229 (1995)Google Scholar
  14. 14.
    Shannon, C.: Programming a computer for playing chess. Philosophical Magazine 41(4), 256 (1950)zbMATHMathSciNetGoogle Scholar
  15. 15.
    Storn, R., Price, K.: Differential Evolution - a simple and efficient adaptive scheme for global optimization over continuous spaces. Technical Report TR-95-012, Berkeley, CA (1995)Google Scholar
  16. 16.
    Storn, R., Price, K.: Differential Evolution – A Simple and Efficient Heuristic for Global Optimization over Continuous Spaces. Journal of Global Optimization 11, 341–359 (1997)zbMATHCrossRefMathSciNetGoogle Scholar
  17. 17.
    Thrun, S.: Learning To Play the Game of Chess. In: Tesauro, G., Touretzky, D., Leen, T. (eds.) Advances in Neural Information Processing Systems 7, pp. 1069–1076. The MIT Press, Cambridge (1995)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2008

Authors and Affiliations

  1. 1.Faculty of Electrical Engineering and Computer ScienceUniversity of MariborMariborSlovenia

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