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Adversarial search by evolutionary computation.

T P Hong1, K Y Huang, W Y Lin

  • 1Department of Electrical Engineering, National University of Kaohsiung, Kaohsiung, Taiwan 811, ROC. tphong@nuk.edu.tw

Evolutionary Computation
|August 28, 2001
PubMed
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This study introduces a novel genetic algorithm for improving next-move selection in two-player games. The enhanced approach boosts solution accuracy and search speed, overcoming limitations of traditional methods.

Area of Science:

  • Artificial Intelligence
  • Game Theory
  • Computational Intelligence

Background:

  • Traditional game search algorithms like minimax and alpha-beta pruning face challenges with temporal and spatial complexity in deep search trees.
  • Genetic algorithms offer global optimization but struggle with compound optima crucial for game-tree search.

Purpose of the Study:

  • To address the limitations of existing algorithms in finding optimal next moves in two-player games.
  • To propose a novel genetic algorithm that enhances both accuracy and speed in game-tree search.

Main Methods:

  • Developed a new genetic algorithm incorporating a strategy to reserve board evaluation values of offspring.
  • Applied this algorithm to a partial game-search tree to identify optimal next moves.

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Main Results:

  • The proposed genetic algorithm significantly improved solution accuracy compared to traditional methods.
  • Experimental results demonstrated a substantial increase in search speed.

Conclusions:

  • The novel genetic algorithm effectively finds superior next moves in two-player games.
  • This approach offers a promising alternative for complex game-tree search problems, enhancing efficiency and performance.