The Improvement Of The Adaptive Genetic Algorithm And Its Application

Simple genetic algorithm is a heuristic searching algorithm. Its scheme of searching for the best result is not perfect. It has some shortages such as slow convergence, bad stability and premature phenomenon in application. Existing adaptive genetic algorithm has local optimization solution. Therefore, it is essential to implement a further research and discussion on the auto-adapted genetic algorithm.In order to solve the disadvantages of simple genetic algorithms and existing adaptive genetic algorithms, we began with analyzing the performance index and the computation load of the population "premature", and then developed a new degree evaluating indicator of new population "premature". It is expected that the present paper will propose a new self-adapted genetic algorithm to reach the satisfactory globally optimal solution, which can not only speed up the genetic evolution speed but also strengthen the corresponding global convergence performance.This article first introduced genetic algorithm's background, the development process and the application, the domestic and foreign present research situation, the research background, the research goal and the anticipated result. Next this thesis explained the simple heredity algorithm and several kinds of improved auto-adapted genetic algorithm, moreover analyzed some existing flaw in the auto-adapted genetic algorithm, laying the foundation for the later work. Finally this article proposed a new kind of determination population "precocious" degree method. The involved idea is making the improvement to the algorithm overlapping probability and the variation probability. Furthermore, it is successful for the design to realize the new algorithm this article proposed. The experimental result showed that the new improved auto-adapted genetic algorithm is of better performance such as good stability, quick convergent speed and so on.