Evolutionary Local Search of Fuzzy Rules through a novel Neuro-Fuzzy encoding method.
This paper proposes a new approach for constructing fuzzy knowledge bases using evolutionary methods. We have designed a genetic algorithm that automatically builds neuro-fuzzy architectures based on a new indirect encoding method. The neuro-fuzzy architecture represents the fuzzy knowledge base that solves a given problem; the search for this architecture takes advantage of a local search procedure that improves the chromosomes at each generation. Experiments conducted both on artificially generated and real world problems confirm the effectiveness of the proposed approach.
Carrascal
A
A
Departamento de Inteligencia Artificial, Facultad de Informática, Universidad Politécnica de Madrid, 28660 Boadilla del Monte, Madrid, Spain. aacarrascal@legazpi.dia.fi.upm.es
Manrique
D
D
Ríos
J
J
Rossi
C
C
D000465:Algorithms; D001185:Artificial Intelligence; D005075:Biological Evolution; D001943:Breast Neoplasms / Q000145:classification; D002965:Classification; D019295:Computational Biology; D017143:Fuzzy Logic; D008957:Models, Genetic