Learning fuzzy rules with evolutionary algorithms - An analytic approach

This paper provides an analytical approach to fuzzy rule base optimization. While most research in the area has been done experimentally, our theoretical considerations give new insights to the task. Using the symmetry that is inherent in our formulation, we show that the problem of finding an optimal rule base can be reduced to solving a set of quadratic equations that generically have a one dimensional solution space. This alternate problem specification can enable new approaches for rule base optimization.Jens Kroeske, Adam Ghandar, Zbigniew Michalewicz and Frank Neuman