Distributed adaptive search method for Genetic Algorithm controlled by fuzzy reasoning
Qiang Li, Yoichiro Maeda · 2008
In this paper, we proposed FASPGA based on diversity measure (DM-FASPGA) and FASPGA based on evolution history (EH-FASPGA) as the improvement method of fuzzy adaptive search method for parallel genetic algorithm (FASPGA). In DM-FASPGA, genetic parameters is tuning by fuzzy rule based on diversity of sub-population. Many kinds of diversity measure parameters are imported into the fuzzy rule. And in EH-FASPGA, we imported the evolution history information for improving the accuracy to estimate the evolution degree. Simulation results are also further presented to show the effectiveness and performance of method we proposed in this paper.