Cooperatively coevolving differential evolution for compensatory neural fuzzy networks

This study presents a cooperatively coevolving differential evolution (CCDE) learning algorithm to optimize the parameters of a compensatory neural fuzzy network (CNFN). CCDE decomposes the fuzzy system into multiple subpopulations where each subpopulation represents a fuzzy rule set, and each individual within each subpopulation evolves by differential evolution (DE) separately. The proposed CCDE uses cooperative behavior among multiple subpopulations for combining their information and building the complete fuzzy system to accelerate the search and increase global search capacity.