A Lower Bound Analysis of Population-Based Evolutionary Algorithms for Pseudo-Boolean Functions

Abstract

Evolutionary algorithms (EAs) are population-based general-purpose optimization algorithms, and have been successfully applied in real-world optimization tasks. However, previous theoretical studies often employ EAs with only a parent or offspring population and focus on specific problems. Furthermore, they often only show upper bounds on the running time, while lower bounds are also necessary to get a complete understanding of an algorithm. In this paper, we analyze the running time of the ($\mu +\lambda$)-EA (a general population-based EA with mutation only) on the class of pseudo-Boolean functions with a unique global optimum. By applying the recently proposed switch analysis approach, we prove the lower bound $\varOmega (n \ln n\,+\,\mu \,+\,\lambda n\ln \ln n/ \ln n)$ for the first time. Particularly on the two widely-studied problems, OneMax and LeadingOnes, the derived lower bound reveals that the ($\mu +\lambda$)-EA will be slower than the (1 + 1)-EA when the population size $\mu$ or $\lambda$ is above a moderate order. Our results imply that the increase of population size, while usually desired in practice, bears the risk of increasing the lower bound of the running time and thus should be carefully considered.

This work was supported by the NSFC (61333014, 61375061), the Jiangsu Science Foundation (BK20160066), the Fundamental Research Funds for the Central Universities (WK2150110002), the 2015 Microsoft Research Asia Collaborative Research Program, and the Collaborative Innovation Center of Novel Software Technology and Industrialization.