The Research On Genetic Algorithm Based On Chaos And Immune

As a new global optimization search method, with its many characteristics like simple and universal, robust, parallelism and extensive use, Genetic Algorithm has been applied in many fields as artificial intelligence and gets a lot of research findings. Based on the study and the research about the genetic algorithm, this thesis proposes a new advanced genetic strategy which is called MICGAFirstly, this thesis introduces the research background and basic search mechanism about genetic algorithm and expounds its traits and applications briefly. But this algorithm itself has some defects like premature and slow convergence to specific problems, etc. To the defects like that, with the thought of inosculation, this advanced algorithm takes two optimization mechanisms, chaos and immune. It looks like that chaos is stochastic and out-of-order, but it is a phenomenon by inherent exquisite structure. Use the stochastic, ergodicity and sensitivity of initial value of chaos to generate population and this process can make the individuals distributed in the space of solutions evenly, avoiding the defects of slow convergence caused by the totally stochastic. Immune mechanism is used to amend the select operator by its density of antigen to maintain the diversity of the population in order to prevent premature convergence. Based on all of those mentioned, it also draws into a new method named schema recognition which is highlighted by the schema to improve the whole population. Its process is that after the mutation operator, the individuals are sorted by their fitness value, then with certain weight-value the best schema in this generation is recognized to improve the whole population, meanwhile it takes anneal principle to avoid degeneration condition. The convergence velocity is fast by these operators. At last, it draws into chaotic perturbation operation to do heuristic mutation to accelerate convergence velocity and improve searching capability. Through some tests on the classic test function with two evaluative criteria, the average truncated generation and the distribution entropy of truncated generations, the test results show that this novel algorithm is effective.