Research On The Niche And Parallel Genetic Algorithm

Genetic algorithm is a kind of random searching method using lives' natural election and genetic mechanism. Its application predominance lies in complicated and non-linear problems, which are difficult for traditional searching. As a kind of excellent tool, genetic algorithm has been widely used in many fields such as combination and optimization, adaptive controlling, machine learning, programming design, artificial life and so on, which will be one of the key techniques in the intelligence calculation.But there is much fault about the genetic algorithm. In the paper, firstly we analyze the schema theory and point out: the shortage of mode patterns leads to early convergence, and the slowly changing of the mode affects the convergence velocity. In order to solve the two questions, we put forward two improved hybrid genetic algorithms: the niche genetic algorithm and the parallel genetic algorithm。By analyzing the theories of GAs, the improved niche genetic algorithm is given to solve early convergence. Optimization rule makes the initialized group diversity; the niche couples strengthen the contact of the two niche groups, by which the best individual can be shared; the self-variable operator improves the searching ability. The analysis of the theories and the result of researches show that the convergent efficiency of the improved algorithm is wholly strengthened.In order to improve the convergent velocity of GAs, a new parallel genetic algorithm is taken into account. The parallel genetic algorithm is an adaptation of the genetic algorithm to parallel computers. Parallel searching greatly overcomes the shortcoming of GAs and time is remarkably reduced.