Fuzzy systems modeling with participatory evolution

Abstract

Participatory evolution is a learning paradigm recently introduced in the realm of fuzzy system modeling and system optimization. The paradigm benefits from the concept of participatory learning, genetic algorithms and differential evolution. In this paper we address two distinct participatory evolutionary learning algorithms. The first combines participatory learning and the processing steps of differential evolution to develop a differential participatory learning approach. The second uses participatory learning and genetic algorithm structure and steps to produce a genetic participatory learning approach. An electric maintenance modeling instance using actual data is tackled by the two approaches to develop linguistic fuzzy rule-based models. Their modeling performance is compared against alternative participatory genetic learning algorithms and a contemporary genetic fuzzy system approach. Experimental results suggest that participatory evolutionary learning algorithms perform better, but the genetic participatory learning approach outperforms current state of art approaches.