A FUZZY EVOLUTIONARY APPROACH TO THE CLASSIFICATION PROBLEM

Genetic algorithms are powerful and robust heuristic adaptation proced ures suggested by biological evolution and molecular genetics. Fuzzy s et theory and fuzzy logic have been proposed in order to provide some means for representing and manipulating imprecision and vagueness. In this paper genetic algorithms and fuzzy logic are combined in a unifor m framework suitable for fuzzy classification. We discuss how a fuzzy classification methodology introduced in previous papers has been impr oved by becoming part of a genetic algorithm. The resulting genetic fu zzy classification technique shows increased sensitivity of solution, avoids the effect of fuzzy numbers grouping and allows for more effect ive search over solution space.