Genetic Algorithm using Theory of Chaos

optimization, genetic algorithm, chaos

This paper is focused on genetic algorithm with chaotic crossover operator. We have performed some experiments to study possible use of chaos in simulated evolution. A novel genetic algorithm with chaotic optimization operation is proposed to optimization of multimodal functions. As the basis of a new crossing operator a simple equation involving chaos is used, concrete the logistic function. The logistic function is a simple one-parameter function of the second order that shows a chaotic behavior for some values of the parameter. Generally, solution of the logistic function has three areas of its behavior: convergent, periodic and chaotic. We have supposed that the convergent behavior leads to exploitation and the chaotic behavior aids to exploration. The periodic behavior is probably neutral and thus it is a negligible one. Results of our experiments conrm these expectations. A proposed genetic algorithm with chaotic crossover operator leads to a more ecient computation in comparison with the traditional genetic algorithm.