Modular Neural Networks architecture optimization with a new nature inspired method using a fuzzy combination of Particle Swarm Optimization and Genetic Algorithms

We describe in this paper a new hybrid approach for optimization combining Particle Swarm Optimization (PSO) and Genetic Algorithms (GAs) using Fuzzy Logic to integrate the results. The new optimization method combines the advantages of PSO and GA to provide 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 FPSO and FGA. The proposed optimization method was tested with a set of benchmark mathematical functions and then with a more complex problem of neural network architecture optimization. The results of the proposed hybrid optimization method are shown to outperform other methods for these problems.