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.



































Similar content being viewed by others
Explore related subjects
Discover the latest articles, news and stories from top researchers in related subjects.References
Shannon C E (1950) XXII. Programming a computer for playing chess. Philos Mag 41:256–275
Mandziuk J (2010) Knowledge-free and learning-based methods in intelligent game playing. Springer, Berlin
Tim Jones M (2008) Artificial intelligence: a systems approach. Jone & Bartlett Learning Publication, ISBN-13: 9780763773373
Bowden B V (1953) Faster than thought. In: A symposium on digital computing machines. Pitman Publishing
Hsu F-H (1999) IBM’s deep blue chess grandmaster chips. IEEE Micro 19:70–81
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 251–253
Hong T-P, Huang K-Y, Lin W-Y (2001) Adversarial search by evolutionary computation. Evol Comput 9:371–385
David O, van den Herik J, Koppel M, Netanyahu N (2014) Genetic algorithms for evolving computer chess programs. In: IEEE transactions on evolutionary computation
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 13:3234–3247
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 1–6
Price K, Storn R (1997) Differential evolution–a simple evolution strategy for fast optimization. Dr. Dobb’s J 22:18–24
Price K, Storn R M, Lampinen J A (2006) Differential evolution: a practical approach to global optimization: Springer Science & Business Media
Ronkkonen J, Kukkonen S, Price K V (2005) Real-parameter optimization with differential evolution. In: IEEE congress on evolutionary computation. Edinburgh, pp 506–513
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 1851–1856
Hauptman A (2005) GP-EndChess: using genetic programming to evolve chess endgame players. In: The 8th European conference on genetic programming. Switzerland, pp 120–131
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 78–89
Laws of Chess. Available: http://www.fide.com/component/handbook/?id=124&view=article, Access Date: 29-7-2016
Veness J, Bair A (2007) Effective use of transposition table in stochastic game tree search. In: IEEE symposium on computational intelligence and games, pp 112–116
Millington I, Funge J (2009) Artificial intelligence for games, 2nd edn. Morgan Kaufmann, Elsevier
http://chessprogramming.wikispaces/UCI, Access Date: 20-2-2016
http://hgm.nubati.net, Access Date: 20-2-2016
Skiena S S (2009) The algorithm design manual. Springer, London
http://chessprogramming.wikispaces/Winglet, Access Date: 20-2-2016
https://stockfishchess.org, Access Date: 20-2-2015
Hellsten J (2010) Mastering chess strategy. Everyman Chess
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– 648
Wilson F, Alberston B (1999) 303 tricky chess tactics. Cardoza Publishing
http://chessprogramming.wikispaces.com/Ufim, Access Date: 29-7-2016
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Dehghani, H., Babamir, S.M. A GA based method for search-space reduction of chess game-tree. Appl Intell 47, 752–768 (2017). https://doi.org/10.1007/s10489-017-0918-z
Published:
Issue Date:
DOI: https://doi.org/10.1007/s10489-017-0918-z