A NOVEL ALGORITHM FOR FUZZY-GENETIC DISTRIBUTED DATA MINING

Abstract

A novel framework for a Parallel Fuzzy-Genetic Algorithm (PFGA) has been developed for classification and prediction over decentralized data sources as a main contribution to the scientific community. The model parameters are evolved using two nested genetic algorithms (GAs). The outer GA evolves the fuzzy sets whereas the inner GA evolves the fuzzy rules. During optimization, best rules are only distributed and exchanged among agents to construct the overall optimized model. Several experiments have been conducted and show that the developed model has good accuracy and more efficient in performance and comprehensibility of linguistic rules compared to some models implemented in KEEL software tool.