Convergence of Factored Evolutionary Algorithms
Shane Strasser, John W. Sheppard · 2017
Factored Evolutionary Algorithms (FEA) have been found to be an effective way to optimize single objective functions by partitioning the variables in the function into overlapping subpopulations, or factors. While there exist several works empirically evaluating FEA, there exists very little literature exploring FEA's theoretical properties. In this paper, we prove that the final solution returned by FEA will be the results of converging to a single point. Additionally, we show how the convergence of FEA to a single point in the search space could be to a suboptimal point in space. However, we demonstrate empirically that when using specific factor architectures, the probability of converging to these suboptimal points in space approaches zero. Finally, where hybrid versions Cooperative Coevolutionary Algorithms have been proposed as a means to escape these suboptimal points, we show how FEA is able to outperform its hybrid version.