PROCEEDINGS IPMU '08
A Multi-Objective Cooperative Coevolutionary Approach to Mamdani Fuzzy System Generation
Alessio Botta, Pietro Ducange, Beatrice Lazzerini, Francesco Marcelloni.
A novel multi-objective cooperative
coevolutionary approach aimed at generating
a set of Mamdani-type fuzzy
rule-based systems (FRBSs) with optimal
trade-offs between accuracy and interpretability
is proposed. Interpretability
is measured both in terms of complexity
of the rule base (RB) and of integrity
of the data base (DB). In the
framework of the cooperative coevolutionary
approach, multi-objective optimization
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.
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