Modification of Evolutionary Multiobjective Optimization Algorithms for Multiobjective Design of Fuzzy Rule-Based Classification Systems

We examine three methods for improving the ability of evolutionary multiobjective optimization (EMO) algorithms to find a variety of fuzzy rule-based classification systems with different tradeoffs with respect to their accuracy and complexity. The accuracy of each fuzzy rule-based classification system is measured by the number of correctly classified training patterns while its complexity is measured by the number of fuzzy rules and the total number of antecedent conditions. One method for improving the search ability of EMO algorithms is to remove overlapping rule sets in the three-dimensional objective space. Another method is to choose similar rule sets as parents for crossover operations. The other method is to bias the selection probability of parents toward rule sets with high accuracy. The effectiveness of each method is examined through computational experiments on benchmark data sets