Resumen de Finding Fuzzy Identification System Parameters Using A New Dynamic Migration Period-Based Distributed Genetic Algorithm
Marco Antonio Castro Liera, Francisco Herrera Fernández
This paper presents a distributed genetic algorithm with dynamic determination of the migration period. The algorithm is especially well suited for the on line estimation of a fuzzy identification system parameters, using heterogeneous clusters. The results of the optimization of a TSK (Takagi-Sugeno-Kang) system for the identification of a biotechnological (fermentative) process including the solution’s quality and speedup analysis are presented. Comparative results using static and dynamic migration periods on the genetic algorithm are also presented.
