Depth-first iterative-deepening: an optimal admissible tree search
Artificial Intelligence
Game tree searching by min/max approximation
Artificial Intelligence
AI Magazine
Control strategies for two-player games
ACM Computing Surveys (CSUR)
The development of a world class Othello program
Artificial Intelligence - Special issue on computer chess
On learning and testing evaluation functions
Journal of Experimental & Theoretical Artificial Intelligence
Multi-player alpha-beta pruning
Artificial Intelligence
Adaptation in natural and artificial systems
Adaptation in natural and artificial systems
Artificial intelligence (3rd ed.)
Artificial intelligence (3rd ed.)
Genetic algorithm for feature selection for parallel classifiers
Information Processing Letters
The multi-player version of minimax displays game-tree pathology
Artificial Intelligence
Fundamentals of algorithmics
An introduction to genetic algorithms
An introduction to genetic algorithms
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Handbook of AI
Designing Neural Networks using Genetic Algorithms
Proceedings of the 3rd International Conference on Genetic Algorithms
Information content of chess positions
ACM SIGART Bulletin
Automatically integrating multiple rule sets in adistributed-knowledge environment
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
Evolution of an efficient search algorithm for the mate-in-N problem in chess
EuroGP'07 Proceedings of the 10th European conference on Genetic programming
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In this paper, we consider the problem of finding good next moves in two-player games. Traditional search algorithms, such as minimax and α-β pruning, suffer great temporal and spatial expansion when exploring deeply into search trees to find better next moves. The evolution of genetic algorithms with the ability to find global or near global optima in limited time seems promising, but they are inept at finding compound optima, such as the minimax in a game-search tree. We thus propose a new genetic algorithm-based approach that can find a good next move by reserving the board evaluation values of new offspring in a partial game-search tree. Experiments show that solution accuracy and search speed are greatly improved by our algorithm.