Evolutionary Local Search of Fuzzy Rules through a Novel Neuro-Fuzzy Encoding Method

Abstract

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.

Comparative study of the prediction of electrical energy from a photovoltaic system using the intelligent systems ANFIS and ANFIS-GA

“…The optimized generation of the candidate solutions is utilized in the next iteration of the algorithm (Carrascal et al, 2009). The GA stops when the optimal solution is obtained.…”