An evolutionary paradigm for designing fuzzy rule-based systems from examples

The main aim of this paper is to present a methodology for designing fuzzy rule-based systems from examples based on evolutionary algorithms. This methodology consists of some design guidelines that allow us to obtain different genetic fuzzy rule-based systems, i.e., evolutionary algorithm-based processes to design fuzzy rule-based systems by learning and/or tuning the knowledge base, following the same generic structure and able to cope with problems of different nature. A specific genetic fuzzy rule-based systems obtained from the paradigm proposed is introduced and its accuracy in the fuzzy modeling of two three-dimensional surfaces is analyzed.