Computable bounds on the rate of convergence in evolutionary computation

The broad field of evolutionary computation (EC)including genetic algorithms as a special case-has attracted much attention in the last several decades. Many bold claims have been made about the effectiveness of various EC algorithms. These claims have centered on the efficiency, robustness, and ease of implementation of EC approaches. Unfortunately, there seems to be little theory to support such claims. One key step to formally evaluating or substantiating such claims is to establish rigorous results on the rate of convergence of EC algorithms. This paper presents a computable rate of convergence for a class of ECs that includes the standard genetic algorithm as a special case