Adaptation in natural and artificial systems
Adaptation in natural and artificial systems
The garden in the machine: the emerging science of artificial life
The garden in the machine: the emerging science of artificial life
Handbook of Evolutionary Computation
Handbook of Evolutionary Computation
Genetic Algorithms: Concepts and Designs with Disk
Genetic Algorithms: Concepts and Designs with Disk
Evolutionary Optimization Versus Particle Swarm Optimization: Philosophy and Performance Differences
EP '98 Proceedings of the 7th International Conference on Evolutionary Programming VII
Genetic Algorithms Reference
Human evolutionary model: A new approach to optimization
Information Sciences: an International Journal
Population structure and particle swarm performance
CEC '02 Proceedings of the Evolutionary Computation on 2002. CEC '02. Proceedings of the 2002 Congress - Volume 02
Population structure and particle swarm performance
CEC '02 Proceedings of the Evolutionary Computation on 2002. CEC '02. Proceedings of the 2002 Congress - Volume 02
The particle swarm - explosion, stability, and convergence in amultidimensional complex space
IEEE Transactions on Evolutionary Computation
IEEE Transactions on Neural Networks
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We describe in this paper a new hybrid approach for optimisation combining Particle Swarm Optimisation (PSO) and Genetic Algorithms (GAs) using Fuzzy Logic for parameter adaptation and to integrate the results. The new evolutionary method combines the advantages of PSO and GA to give us an improved FPSO + FGA hybrid method. Fuzzy Logic is used to combine the results of the PSO and GA in the best way possible. Also, fuzzy logic is used to adjust parameters in the FPSO and FGA. The new hybrid FPSO + FGA approach is compared with the PSO and GA methods with a set of benchmark mathematical functions. The proposed hybrid method is also tested with the problem of neural network architecture optimisation. The new hybrid FPSO + FGA method is shown to be superior with respect to the individual evolutionary methods. The tests were made with 2, 4, 8 and 16 variables.