Using chaos in genetic algorithms

We have performed several experiments to study the possible use of chaos in simulated evolution. Chaos is often associated with dynamic situations in which there is feedback, hence there is speculation in the literature that chaos is a factor in natural evolution. We chose the iterated prisoner´s dilemma problem as a test case, since it is a dynamic environment with feedback. To further illustrate the benefits of employing chaos in genetic algorithms, data derived from a genetic data clustering algorithm under development at the Idaho National Engineering and Environmental Laboratory is also presented. To perform an initial assessment of the use of chaos, we used the logistic function, a simple equation involving chaos, as the basis of a special mutation operator, which we call λ mutation. The behavior of the logistic function is well known and comprises three characteristic ranges of operation: convergent, bifurcating, and chaotic. We hypothesize that the chaotic regime will aid exploration, and the convergent range will aid exploitation. The bifurcating range is likely neutral, and hence an insignificant factor. Our results confirm these expectations