Classifier Fitness Based on Accuracy

Stewart W. Wilson
1995 Evolutionary Computation  
In many classifier systems, the classifier strength parameter serves as a predictor of future payoff and as the classifier's fitness for the genetic algorithm. We investigate a classifier system, XCS, in which each classifier maintains a prediction of expected payoff, but the classifier's fitness is given by a measure of the prediction's accuracy. T h e system executes the genetic algorithm in niches defined by the match sets, instead of panmictically. These aspects of XCS result in its
more » ... on tending to form a complete and accurate mapping X x A + P from inputs and actions to payoff predictions. Further, XCS tends to evolve classifiers that are maximally general, subject to an accuracy criterion. Besides introducing a new direction for classifier system research, these properties of XCS make it suitable for a wide range of reinforcement learning situations where generalization over states is desirable.
doi:10.1162/evco.1995.3.2.149 fatcat:h5t5olb5pjbi3nn25oxahl3zcy