Generating linguistic fuzzy rules for pattern classification with genetic algorithms

This paper presents a new genetic-based approach to automatically extracting classification knowledge from numerical data by means of premise learning. A genetic algorithm is utilized to search for premise structure in combination with parameters of membership functions of input fuzzy sets to yield optimal conditions of classification rules. The consequence under a specific condition is determined by choosing from all possible candidates the class which lead to a maximal truth value of the rule. The major advantage of our work is that a parsimonious knowledge base with a low number of classification rules is made possible. The effectiveness of the proposed method is demonstrated by the simulation results on the Iris data.