Absumption based on overgenerality and condition-clustering based specialization for XCS with continuous-valued inputs
Hiroki Shiraishi, Yohei Hayamizu, Hiroyuki Satō, Keiki Takadama · Proceedings of the Genetic and Evolutionary Computation Conference · 2022
This paper focuses on the concept of "absumption" which restrains over-general rules by decomposing them into several concrete rules, proposes the novel "absumption" for continuous spaces by improving the conventional absumption to achieve high performance (e.g., the acquired rewards) in a noisy environment, and integrates it into the XCS for real-valued inputs (XCSR) to evaluate it through a comparison with the conventional absumption. Concretely, the proposed absumption mechanism based on Overgenerality and Condition-clustering based specialization (called Absumption-OC) manipulates the balance between the Overgenerality of the rules and the specialization of Condition of the rules. Through the intensive experiments of three different types of continuous space problems, the following implications have been revealed: (1) XCSR with Absumption-OC shows the statistically significant performance in the acquired reward, the system error, and the population size against XCSR with the conventional absumption; and (2) this effectiveness of Absumption-OC becomes to be clear in noisy reward environments in comparison within noiseless reward environments.