Automatic adjustment of selection pressure based on range of reward in learning classifier system
Takato Tatsumi, Hiroyuki Satō, Keiki Takadama · Proceedings of the Genetic and Evolutionary Computation Conference · 2017
XCS (Accuracy-based learning classifier system) can acquire accurate classifiers on the basis of consistent reward, but it does not always receive the consistent reward in real world problems even if it provides the same output for the same input. Such a situation prevents XCS from reducing the number of overspecific accurate classifiers by the subsumption mechanism. This means that XCS finds it hard to acquire the optimal classifiers. For this issue, our previous research proposed XCS-MR (XCS based on Mean of Reward) which can reduce the number of classifiers even in the environments where the size of the rewards is uncertain. However, XCS-MR requires a large amount of learning data to correctly determine the accuracy of classifiers because XCS-MR needs to record the average and variance of the rewards in all input-output space. To overcome this problem, this paper proposes a new XCS that can reduce the number of the classifiers even in the uncertain reward environments without recording the average and variance of the rewards in all input-output space. This paper shows the effectiveness of the proposed XCS through the experiments.