Title: Evolutionary method combining Particle Swarm Optimisation and Genetic Algorithms using fuzzy logic for parameter adaptation and aggregation: the case neural network optimisation for face recognition
Authors: Fevrier Valdez, Patricia Melin, Oscar Castillo
Addresses: Computer Science in the Graduate Division, Tijuana Institute of Technology, Tijuana, BC 22379, Mexico. ' Computer Science in the Graduate Division, Tijuana Institute of Technology, Tijuana, BC 22379, Mexico. ' Computer Science in the Graduate Division, Tijuana Institute of Technology, Tijuana, BC 22379, Mexico
Abstract: 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.
Keywords: PSO; particle swarm optimisation; GAs; genetic algorithms; fuzzy logic; parameter adaptation; parameter aggregation; neural networks; optimisation; face recognition.
International Journal of Artificial Intelligence and Soft Computing, 2010 Vol.2 No.1/2, pp.77 - 102
Published online: 04 Apr 2010 *Full-text access for editors Access for subscribers Purchase this article Comment on this article