Abstract
Genetic Algorithm (GA) has been successfully applied in wide scope, and is a learning algorithm to mimic the biological mechanism of inheritance (neo-Darwinism). In general, because GA is an exploration method including stochastic search, there were a number of issues. Specially, the search ability of ordinary GA is not always optimal in the early and final stage of search, because of fixed genetic parameters, i.e., crossover rate, mutation rate and so on. Therefore, we have already proposed the fuzzy adaptive search method for parallel genetic algorithm based on the acceleration of evolution and high quality solutions. However, there are some cases when it is not enough accuracy to describe the stage of evolution, because the best fitness and average fitness were adopted as inputs of fuzzy rules. Moreover, worse performance was shown in the test function with high dimensions. Therefore, in this research we propose the improvement methods that have a good performance in the optimization problem of high-dimensional function. And the comparison simulations are executed to verify the efficiency of proposed methods. The results of simulations are also reported.
