Advertisement

Soft Computing

, Volume 15, Issue 4, pp 667–683 | Cite as

History mechanism supported differential evolution for chess evaluation function tuning

  • B. Bošković
  • J. Brest
  • A. Zamuda
  • S. Greiner
  • V. Žumer
Original Paper
First Online:

Abstract

This paper presents a differential evolution (DE) based approach to chess evaluation function tuning. DE with opposition-based optimization is employed and upgraded with a history mechanism to improve the evaluation of individuals and the tuning process. The general idea is based on individual evaluations according to played games through several generations and different environments. We introduce a new history mechanism which uses an auxiliary population containing good individuals. This new mechanism ensures that good individuals remain within the evolutionary process, even though they died several generations back and later can be brought back into the evolutionary process. In such a manner the evaluation of individuals is improved and consequently the whole tuning process.

Keywords

Chess evaluation function tuning Differential evolution History mechanism Opposition-based optimization 
This is a preview of subscription content, log in to check access.

Notes

Acknowledgments

The authors would like to thank Rémi Coulom for Bayeselo program, Daniel Shawul for bitbases, Marc Lacrosse for performance.bin opening book, Prof. Robert Hyatt for Crafty program, Chua Kong Sian and Stuart Cracraft for GNU Chess program and Rainer Storn for the C code of the DE. The authors would also like to thank reviewers for their detailed and constructive comments that helped us to increase the quality of this paper.

References

  1. Anantharaman TS (1990) A statistical study of selective min–max search in computer chess. Ph.D. thesis, Carnegie Mellon University Pittsburgh, USAGoogle Scholar
  2. Anantharaman TS (1997) Evaluation tuning for computer chess: linear discriminant methods. ICCA J 20(4):224–242Google Scholar
  3. Baxter J, Tridgell A, Weaver L (1998) Experiments in parameter learning using temporal differences. Int Comput Chess Assoc J 21(2):84–99Google Scholar
  4. Baxter J, Tridgell A, Weaver L (2000) Learning to play chess using temporal differences. Mach Learn 40(3):243–263MATHCrossRefGoogle Scholar
  5. Beal DF, Smith MC (1997) Learning piece values using temporal differences. J Int Comput Chess Assoc 20(3):147–151Google Scholar
  6. Beal DF, Smith MC (1999) Learning piece-square values using temporal differences. ICCA J 22(4):223–235Google Scholar
  7. Bošković B, Greiner S, Brest J, Žumer V (2006) A differential evolution for the tuning of a chess evaluation function. In: The 2006 IEEE congress on evolutionary computation CEC2006. IEEE Press, New Jersey, pp 6742–6747Google Scholar
  8. Bošković B, Greiner S, Brest J, Zamuda A, Žumer V (2008) An adaptive differential evolution algorithm with opposition-based mechanisms, applied to the tuning of a chess program. In: Chakraborty UK (ed) Advances in differential evolution, studies in computational intelligence, vol 143. Springer, BerlinGoogle Scholar
  9. Brest J, Maučec MS (2008) Population size reduction for the differential evolution algorithm. Appl Intell 29(3):228–247CrossRefGoogle Scholar
  10. Brest J, Greiner S, Bošković B, Mernik M, Žumer V (2006) Self-adapting control parameters in differential evolution: a comparative study on numerical benchmark problems. IEEE Trans Evol Comput 10(6):646–657CrossRefGoogle Scholar
  11. Brest J, Bošković B, Greiner S, Žumer V, Maučec MS (2007) Performance comparison of self-adaptive and adaptive differential evolution algorithms. Soft Comput Fusion Found Methodol Appl 11(7):617–629MATHGoogle Scholar
  12. Caponio A, Neri F, Tirronen V (2009) Super-fit control adaptation in memetic differential evolution frameworks. Soft Comput 13(8–9):811–831CrossRefGoogle Scholar
  13. Chellapilla K, Fogel DB (1999) Evolving neural networks to play checkers without relying on expert knowledge. IEEE Trans Neural Netw 10(6):1382–1391CrossRefGoogle Scholar
  14. Chellapilla K, Fogel DB (2001) Evolving an expert checkers playing program without using human expertise. IEEE Trans Evol Comput 5(4):422–428CrossRefGoogle Scholar
  15. Clark D (1997) Deep thoughts on deep blue. IEEE Exp Intell Syst Appl 12(4):31Google Scholar
  16. Demšar J (2006) Statistical comparisons of classifiers over multiple data sets. J Mach Learn Res 7:1–30MathSciNetGoogle Scholar
  17. Feoktistov V (2006) Differential evolution: in search of solutions (Springer optimization and its applications). Springer, New YorkMATHGoogle Scholar
  18. Fogel DB (2006) Evolutionary computation: toward a new philosophy of machine intelligence, 3rd edn. Wiley-IEEE Press, New JerseyGoogle Scholar
  19. Fogel DB, Hays TJ, Hahn SL, Quon J (2004) A self-learning evolutionary chess program. Proc IEEE 92(12):1947–1954CrossRefGoogle Scholar
  20. Fogel DB, Hays TJ, Hahn SL, Quon J (2006) The Blondie25 chess program competes against Fritz 8.0 and a human chess master. In: Louis SJ, Kendall G (eds) The 2006 IEEE symposium on computational intelligence and games. IEEE Press, New Jersey, pp 230–235Google Scholar
  21. García S, Herrera F (2008) An extension on “statistical comparisons of classifiers over multiple data sets” for all pairwise comparisons. J Mach Learn Res 9:2677–2694Google Scholar
  22. Gomboc D, Buro M, Marsland TA (2005) Tuning evaluation functions by maximizing concordance. Theor Comput Sci 349(2):202–229MathSciNetMATHCrossRefGoogle Scholar
  23. Heinz EA (1999) Scalable search in computer chess: algorithmic enhancements and experiments at high search depths (computational intelligence). Morgan Kaufmann, San FranciscoGoogle Scholar
  24. Hsu FH, Anantharaman TS, Campbell MS, Nowatzyk A (1990) Deep thought. In: Marsland TA, Schaeffer J (eds) Computers, chess and cognition, chap 5. Springer, Berlin, pp 55–78Google Scholar
  25. Hunter DR (2004) MM algorithms for generalized Bradley–Terry models. Ann Stat 32(1):384–406MathSciNetMATHCrossRefGoogle Scholar
  26. Fürnkranz J (2001) Machine learning in games: a survey. In: Fürnkranz J, Kubat M (eds) Machines that learn to play games. Nova Science Publishers, Inc., Hauppauge, pp 11–59Google Scholar
  27. Kendall G, Whitwell G (2001) An evolutionary approach for the tuning of a chess evaluation function using population dynamics. In: Proceedings of the 2001 congress on evolutionary computation CEC2001. IEEE Press, COEX, World Trade Center, 159 Samseong-dong, Gangnam-gu, Seoul, Korea, pp 995–1002Google Scholar
  28. Lacrosse M (2008) Private communicationGoogle Scholar
  29. Levinson R, Snyder R (1991) Adaptive pattern-oriented chess. AAAI pp 601–606Google Scholar
  30. Mysliwietz P (1994) Konstruktion und Optimierung von Bewertungsfunktionen beim Schach.(1994). Ph.D. thesis, University of Paderborn, GermanyGoogle Scholar
  31. Nasreddine H, Poh H, Kendall G (2006) Using an evolutionary algorithm for the tuning of a chess evaluation function based on a dynamic boundary strategy. In: Proceedings of 2006 IEEE international conference on cybernetics and intelligent systems (CIS2006), pp 1–6Google Scholar
  32. Piccinini G (2003) Alan turing and the mathematical objection. Minds Mach 13(1):23–48MathSciNetMATHCrossRefGoogle Scholar
  33. Price KV, Storn RM, Lampinen JA (2005) Differential evolution. A practical approach to global optimization. Springer, BerlinGoogle Scholar
  34. Qin AK, Huang VL, Suganthan PN (2009) Differential evolution algorithm With strategy adaptation for global numerical optimization. IEEE Trans Evol Comput 13(2):398–417CrossRefGoogle Scholar
  35. Quian B, Wang L, Huang DX, Wang X (2009) Multi-objective no-wait flow-shop scheduling with a memetic algorithm based on differential evolution. Soft Comput 13(8–9):847–869CrossRefGoogle Scholar
  36. Rahnamayan S, Tizhoosh HR, Salama MMA (2006) Opposition-based differential evolution algorithms. In: The 2006 IEEE congress on evolutionary computation CEC 2006, pp 7363–7370Google Scholar
  37. Rahnamayan S, Tizhoosh HR, Salama MMA (2008) Opposition-based differential evolution. IEEE Trans Evol Comput 12(1):64–79CrossRefGoogle Scholar
  38. Samuel AL (1959) Some studies in machine learning using the game of checkers. IBM J Res Development 3(3):211–229MathSciNetGoogle Scholar
  39. Samuel AL (1967) Some studies in machine learning using the game of checkers. II—recent progress. IBM J Res Development 11(6):601–617CrossRefGoogle Scholar
  40. Shannon C (1950) Programming a computer for playing chess. Philos Mag 41(4):256MathSciNetMATHGoogle Scholar
  41. Shih FY, Edupuganti VG (2009) A differential evolution based algorithm for breaking the visual steganaliytic system. Soft Comput 13(4):345–353CrossRefGoogle Scholar
  42. Storn R, Price K (1995) Differential evolution—a simple and efficient adaptive scheme for global optimization over continuous spaces. Technical Report TR-95-012, Berkeley, CA, USAGoogle Scholar
  43. Storn R, Price K (1997) Differential evolution—a simple and efficient heuristic for global optimisation over continuous spaces. J Global Optim 11:341–359MathSciNetMATHCrossRefGoogle Scholar
  44. Teng NS, Teo J, Hijazi MHA (2009) Self-adaptive population sizing for a tune-free differential evolution. Soft Comput 13(7):709–724CrossRefGoogle Scholar
  45. Teo J (2006) Exploring dynamic self-adaptive populations in differential evolution. Soft Comput 10(8):673–686CrossRefGoogle Scholar
  46. Tesauro G (2001) Comparison training of chess evaluation functions. In: Furnkranz J, Kubat M (eds) Machines that learn to play games. Nova Science Publishers, Hauppauge, pp 117–130Google Scholar
  47. Thrun S (1995) Learning to play the game of chess. In: Tesauro G, Touretzky D, Leen T (eds) Advances in neural information processing systems 7. The MIT Press, Cambridge, MA, pp 1069–1076Google Scholar
  48. Tizhoosh HR (2005) Opposition-based learning: a new scheme for machine intelligence. In: Proceedings of international conference on computational intelligence for modelling control and automation—CIMCA 2005, Vienna, Austria, pp 695–701Google Scholar

Copyright information

© Springer-Verlag 2010

Authors and Affiliations

  • B. Bošković
    • 1
  • J. Brest
    • 1
  • A. Zamuda
    • 1
  • S. Greiner
    • 1
  • V. Žumer
    • 1
  1. 1.Faculty of Electrical Engineering and Computer ScienceUniversity of MariborMariborSlovenia

Personalised recommendations