A Multi-Objective Cooperative Coevolutionary Approach to Mamdani Fuzzy System Generation
Alessio Botta, Pietro Ducange, Beatrice Lazzerini, Francesco Marcelloni · 2008
A novel multi-objective cooperative coevolutionary approach aimed at gen-erating a set of Mamdani-type fuzzy rule-based systems (FRBSs) with opti-mal trade-offs between accuracy and in-terpretability is proposed. Interpretabil-ity is measured both in terms of com-plexity of the rule base (RB) and of in-tegrity of the data base (DB). In the framework of the cooperative coevolu-tionary approach, multi-objective opti-mization of RB and DB is performed in two distinct populations. Individuals of the two populations cooperate among them through representatives properly extracted at each generation. Results of the application of our approach to the well-known Mackey-Glass chaotic time series dataset are shown and discussed.