The Research Of Several New Kinds Of Hybrid Genetic Algorithm

Genetic algorithm(GA) plays an important role in the fields of computational intelligence, artificial life, image processing, pattern recognition, etc. Actually, accelerating the convergence speed of GA is the main aspect of improving the performance of GA in order to advance its global convergence quality. Hybrid genetic algorithm joined with optimization algorithms and heuristics algorithms is an effective way to advance its running efficiency and solution quality of GA. In this paper, the control mechanism and the application of hybrid genetic algorithm are analyzed and studied. Several new kinds of hybrid genetic algorithm are introduced. The theoretical analysis of these algorithms is given out. The procedural models are designed. And the experimental testing is carried out. The main important work of this paper includes:(1) As circular evolution process can boost up and maintain the diversity of biologic population, a novel simulated annealing genetic algorithm based on circular strategy is proposed. It combines circular strategy with simulated annealing genetic algorithm efficiently. The theoretical analysis and the experimental testing show that, it can not only assure the capability of global convergence, but also accelerate the evolution of colony and acquire the satisfactory global optimal solution.(2) In order to apply quantum genetic algorithm to the multi-peak continuous function optimal problem, a novel quantum genetic algorithm referring to the multi-variable problem is proposed. It operates variables upon a universal quantum chromosome collectively. Thus it can adjust evolutionary intensity dynamically and consider evolutionary orientation depending on generated function of rotation angle. The theoretical analysis and the experimental testing show that, this quantum genetic algorithm can obtain better evolutionary speed and better performance of convergence on the multi-variable problem.(3) Considering different quantum bit having different effective intensity in the chromosome evolution, the potential of a quantum bit is defined and a novel quantum genetic algorithm based on potential of the quantum bit is proposed. The experimental testing shows that, it can obtain better convergence rate and have less runtime on smaller population size and shorter chromosome length.In the paper, the designing idea and experimental algorithms of the hybrid evolutionary computational method mentioned above are described and its convergence performance and computational effect are analyzed based on the theory of matrix and Markov chain. Delphi is used as the experimental platform to verify the research. The theoretical analysis and the experimental testing show that, for different optimization motive, it can effectively improve the defect of traditional GA on computational efficiency and global searching ability, and expand its range and effect in use.