Genetic programming: on the programming of computers by means of natural selection
Genetic programming: on the programming of computers by means of natural selection
Genetic algorithms + data structures = evolution programs (3rd ed.)
Genetic algorithms + data structures = evolution programs (3rd ed.)
Representations and solutions for game-theoretic problems
Artificial Intelligence - Special issue on economic principles of multi-agent systems
Co-Evolution in the Successful Learning of Backgammon Strategy
Machine Learning
Metamagical Themas: Questing for the Essence of Mind and Pattern
Metamagical Themas: Questing for the Essence of Mind and Pattern
Competitive Environments Evolve Better Solutions for Complex Tasks
Proceedings of the 5th International Conference on Genetic Algorithms
New methods for competitive coevolution
Evolutionary Computation
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We present an asymmetric co-evolutionary learning algorithm for imperfect-information zero-sum games. This algorithm is designed so that the fitness of the individual agents is calculated in a way that is compatible with the goal of game-theoretic optimality. This compatibility has been somewhat lacking in previous co-evolutionary approaches, as these have often depended on unwarranted assumptions about the absolute and relative strength of players. Our algorithm design is tested on a game for which the optimal strategy is known, and is seen to work well.