An improved chaos immune genetic algorithm

In the paper we present an improving immune genetic algorithm based on chaos theory. The over-spread character and randomness of chaos can be used to initialize population and improve the searching speed, and the initial value sensitivity of chaos can be used to enlarge the searching space. To avoid the local optimization, the algorithm renews population and enhances the diversity of population by using density calculation of immune theory and adjusting new chaos sequence. To increase precision after the overlapping and mutation, the chaos also is adopted to carry on the local optimization nearby the optimal solution. The experimental results show that the immune genetic algorithm based on chaos theory can search the result of the optimization and evidently improve the convergent speed and astringency.