Fuzzy Rule Bases Automated Design with Self-configuring Evolutionary Algorithm
Eugene Stanislavovich Semenkin, Становов Владимир Вадимович · 2014
Self-configuring evolutionary algorithm of fuzzy rule bases automated deign for solving classification problems, which combines Pittsburgh and Michigan approaches, is introduced. The evolutionary algorithm is based on the Pittsburgh approach where every individual is a rule base and the Michigan approach is used as a mutation operator. A self-configuration method is used to adjust probabilities of the usage of selection, mutation and Michigan part operators. Testing the algorithm on a number of real-world problems demonstrates its efficiency comparing to several other commonly used approaches.