Can the same rule representation change its matching area?
Hiroki Shiraishi, Yohei Hayamizu, Hiroyuki Satō, Keiki Takadama · Proceedings of the Genetic and Evolutionary Computation Conference · 2022
This paper focuses on the rule representation in Learning Classifier System (LCS) and proposes a flexible representation mechanism that can generate a variety of shapes of its matching area with one rule condition of a classifier. Concretely, the proposed representation mechanism changes the shape of the matching area according to the logical product or multiplication of the values of the probability distribution in the multiple dimension. As one of its implementation, this paper introduces the beta distribution in XCS for continuous space. Through intensive experiments of different types of continuous space problems, the following implications have been revealed: XCS based on the beta distribution (1) can match line and curved shapes using the same classifier; (2) can obtain higher reward values with the same or fewer classifiers than the conventional representations (i.e., the hyperrectangular and hyperellipsoidal representations); and (3) is robust to variations in the shape of the class boundary, contributing to stable classification performance.