A GA based method for search-space reduction of chess game-tree
- 252 Downloads
- 1 Citations
Abstract
In this study, a GA (Genetic Algorithm) basesented to reduce the chess game tree space. GA is exploited in some studies and by chess engines in order to: 1) tune the weights of the chess evaluation function or 2) to solve particular problems in chess like finding mate in number of moves. Applying GA for reducing the search space of the chess game tree is a new idea being proposed in this study. A GA-based chess engine is designed and implemented where only the branches of the game tree produced by GA are traversed. Improvements in the basic GA to reduce the problem of GA tactic are evident here. To evaluate the efficiency of this new proposed chess engine, it is matched against an engine where the Alpha-Beta pruning and Min-Max algorithm are applied.
Keywords
Chess game tree Genetic algorithm Alpha-Beta pruning Min-Max algorithmReferences
- 1.Shannon C E (1950) XXII. Programming a computer for playing chess. Philos Mag 41:256–275MathSciNetCrossRefMATHGoogle Scholar
- 2.Mandziuk J (2010) Knowledge-free and learning-based methods in intelligent game playing. Springer, BerlinCrossRefMATHGoogle Scholar
- 3.Tim Jones M (2008) Artificial intelligence: a systems approach. Jone & Bartlett Learning Publication, ISBN-13: 9780763773373Google Scholar
- 4.Bowden B V (1953) Faster than thought. In: A symposium on digital computing machines. Pitman PublishingGoogle Scholar
- 5.Hsu F-H (1999) IBM’s deep blue chess grandmaster chips. IEEE Micro 19:70–81CrossRefGoogle Scholar
- 6.Dehghani H, Babamir SM (2015) Effectiveness analysis of genetic algorithm for chess game tree search. In: The 8th international conference of iranian operations research society, pp 251–253Google Scholar
- 7.Hong T-P, Huang K-Y, Lin W-Y (2001) Adversarial search by evolutionary computation. Evol Comput 9:371–385CrossRefGoogle Scholar
- 8.David O, van den Herik J, Koppel M, Netanyahu N (2014) Genetic algorithms for evolving computer chess programs. In: IEEE transactions on evolutionary computationGoogle Scholar
- 9.Vázquez-Fernández E, Coello C A C, Troncoso F D S (2013) An evolutionary algorithm with a history mechanism for tuning a chess evaluation function. Appl Soft Comput 13:3234–3247CrossRefGoogle Scholar
- 10.Nasreddine H, Poh H S, Kendall G (2006) Using an evolutionary algorithm for the tuning of a chess evaluation function based on a dynamic boundary strategy. In: IEEE conference on cybernetics and intelligent systems, pp 1–6Google Scholar
- 11.Price K, Storn R (1997) Differential evolution–a simple evolution strategy for fast optimization. Dr. Dobb’s J 22:18–24MATHGoogle Scholar
- 12.Price K, Storn R M, Lampinen J A (2006) Differential evolution: a practical approach to global optimization: Springer Science & Business MediaGoogle Scholar
- 13.Ronkkonen J, Kukkonen S, Price K V (2005) Real-parameter optimization with differential evolution. In: IEEE congress on evolutionary computation. Edinburgh, pp 506–513Google Scholar
- 14.Boskovic B, Greiner S, Brest J, Zumer V (2006) A differential evolution for the tuning of a chess evaluation function. In: IEEE congress on evolutionary computation. Vancouver, pp 1851–1856Google Scholar
- 15.Hauptman A (2005) GP-EndChess: using genetic programming to evolve chess endgame players. In: The 8th European conference on genetic programming. Switzerland, pp 120–131Google Scholar
- 16.Hauptman A, Sipper M (2007) Evolution of an efficient search algorithm for the mate-in-N problem in chess. In: The 10th European conference on genetic programming, pp 78–89Google Scholar
- 17.Laws of Chess. Available: http://www.fide.com/component/handbook/?id=124&view=article, Access Date: 29-7-2016
- 18.Veness J, Bair A (2007) Effective use of transposition table in stochastic game tree search. In: IEEE symposium on computational intelligence and games, pp 112–116Google Scholar
- 19.Millington I, Funge J (2009) Artificial intelligence for games, 2nd edn. Morgan Kaufmann, ElsevierGoogle Scholar
- 20.http://chessprogramming.wikispaces/UCI, Access Date: 20-2-2016
- 21.http://hgm.nubati.net, Access Date: 20-2-2016
- 22.Skiena S S (2009) The algorithm design manual. Springer, LondonMATHGoogle Scholar
- 23.http://chessprogramming.wikispaces/Winglet, Access Date: 20-2-2016
- 24.https://stockfishchess.org, Access Date: 20-2-2015
- 25.Hellsten J (2010) Mastering chess strategy. Everyman ChessGoogle Scholar
- 26.Chabris C F, Hearst E S (2003) Visualization, pattern recognition, and forward search: effects of playing speed and sight of the position on grandmaster chess errors. Cogn Sci 27:637– 648CrossRefGoogle Scholar
- 27.Wilson F, Alberston B (1999) 303 tricky chess tactics. Cardoza PublishingGoogle Scholar
- 28.http://chessprogramming.wikispaces.com/Ufim, Access Date: 29-7-2016