Predicting Convergence Time for Genetic Algorithms

It is di cult to predict a genetic algorithm's behavior on an arbitrary problem. Combining genetic algorithm theory with practice we use the average hamming distance as a syntactic metric to derive bounds on the time convergence of genetic algorithms. Analysis of a at function provides worst case time complexity for static functions. Further, employing linearly computable runtime information, we provide bounds on the time beyond which progress is unlikely on arbitrary static functions. As a byproduct, this analysis also provides qualitative bounds by predicting average tness.