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Pareto coevolution

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        <i>Abstract</i>
      
      
        
      
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        When using an automatic discovery method to find a good strategy in a game, we hope to 
        find one that performs well against a wide variety of opponents. An appealing notion in 
        the use of evolutionary algorithms to coevolve strategies is that the population 
        represents a set of different strategies against which a player must do well. Implicit 
        here is the idea that different players represent different “dimensions” of the domain, 
        and being a robust player means being good in many (preferably all) dimensions of the 
        game. Pareto coevolution makes this idea of “players as dimensions” explicit. By 
        explicitly treating each player as a dimension, or objective, we may then use established 
        multi-objective optimisation techniques to find robust strategies. In this paper we apply 
        Pareto coevolution to Texas Hold’em poker, a complex real-world game of imperfect 
        information. The performance of our Pareto coevolution algorithm is compared with that of 
        a conventional genetic algorithm and shown to be promising. . 
      
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