Genetic algorithms are powerful and robust heuristic adaptation procedures suggested by biological evolution and molecular genetics. Fuzzy set 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 uniform framework suitable for fuzzy classification. We discuss how a fuzzy classification methodology introduced in previous papers has been improved by becoming part of a genetic algorithm. The resulting genetic fuzzy classification technique shows increased sensitivity of solution, avoids the effect of fuzzy numbers grouping and allows for more effective search over solution space.
A fuzzy evolutionary approach to the classification problem
CICALESE, Ferdinando;LOIA, Vincenzo
1998-01-01
Abstract
Genetic algorithms are powerful and robust heuristic adaptation procedures suggested by biological evolution and molecular genetics. Fuzzy set 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 uniform framework suitable for fuzzy classification. We discuss how a fuzzy classification methodology introduced in previous papers has been improved by becoming part of a genetic algorithm. The resulting genetic fuzzy classification technique shows increased sensitivity of solution, avoids the effect of fuzzy numbers grouping and allows for more effective search over solution space.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
