Temperature control of rapid thermal processing system using adaptive fuzzy network
Fuzzy Sets and Systems
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Handbook of Evolutionary Computation
Handbook of Evolutionary Computation
IEEE Transactions on Evolutionary Computation
The particle swarm - explosion, stability, and convergence in amultidimensional complex space
IEEE Transactions on Evolutionary Computation
Genetic reinforcement learning through symbiotic evolution forfuzzy controller design
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
A hybrid of genetic algorithm and particle swarm optimization for recurrent network design
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Designing fuzzy logic controllers using a multiresolutional search paradigm
IEEE Transactions on Fuzzy Systems
An online self-constructing neural fuzzy inference network and its applications
IEEE Transactions on Fuzzy Systems
IEEE Transactions on Fuzzy Systems
Evolutionary learning of fuzzy logic controllers and their adaptation through perpetual evolution
IEEE Transactions on Fuzzy Systems
Zero-order TSK-type fuzzy system learning using a two-phase swarm intelligence algorithm
Fuzzy Sets and Systems
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A new approach that automates the design of Fuzzy systems by hybridizing Multi-group Genetic Algorithm and Particle Swarm Optimization, called F-MGAPSO, is proposed in this paper. By F-MGAPSO, we aim to simultaneously design the number of fuzzy rules and free parameters in a fuzzy system. In the initial population of the conceived GA model, the number of rules encoded in each individual is randomly assigned, and the individuals with equal number of rules constitute the same group. These groups will compete against with each other, and the superior ones will gradually prevail over inferiors. Evolution of population consists of three major operations: group enhancement, variable-length individual crossover and mutation. Group enhancement is to enhance elites in each group by a local-neighborhood version of particle swarm optimization. By performing variable-length individual crossover and mutation operations on elites of the same or different groups, we create offsprings, and non-elites in the old population are replaced by these newly bred ones. Performance of F-MGAPSO is verified through simulations and comparisons with other types of genetic algorithms.