A FUZZY SELF-TUNING PARALLEL GENETIC ALGORITHM FOR OPTIMIZATION

The genetic algorithm (GA) is now a very popular tool for solving opti mization problems. Each operator has its special approach route to a s olution. For example, a GA using crossover as its major operator arriv es at solutions depending on its initial conditions. In other words, a GA with multiple operators should be more robust in global search. Ho wever, a multiple operator GA needs a large population size thus takin g a huge time for evaluation. We therefore apply fuzzy reasoning to gi ve effective operators more opportunity to search while keeping the ov erall population size constant. We propose a fuzzy self-tuning paralle l genetic algorithm (FPGA) for optimization problems. In our test case FPGA there are four operators-crossover, mutation, sub-exchange, and sub-copy. These operators are modified using the eugenic concept under the assumption that the individuals with higher fitness values have a higher probability of breeding new better individuals. All operators are executed in each generation through parallel processing, but the p opulations of these operators are decided by fuzzy reasoning. The fuzz y reasoning senses the contributions of these operators, and then deci des their population sizes. The contribution of each operator is defin ed as an accumulative increment of fitness value due to each operator' s success in searching. We make the assumption that the operators that give higher contribution are more suitable for the typical optimizati on problem. The fuzzy reasoning is built under this concept and adjust s the population sizes in each generation. As a test case, a FPGA is a pplied to the optimization of the fuzzy rule set for a model reference adaptive control system. The simulation results show that the FPGA is better at finding optimal solutions than a traditional GA. Copyright (C) 1996 Elsevier Science Ltd