Self-adaptation of XCS learning parameters based on learning theory
Motoki Horiuchi, Masaya Nakata · 2020
This paper proposes a self-adaptation technique of parameter settings used in the XCS learning scheme. Since we adaptively set those settings to their optimum values derived by the recent XCS learning theory, our proposal does not require any trial and error process to find their proper values. Thus, our proposal can always satisfy the optimality of XCS learning scheme, i.e. to distinguish accurate rules from inaccurate rules with the minimum update number of rules. Experimental results on artificial classification problems including overlapping problems show that XCS with our self-adaptation technique significantly outperforms the standard XCS.