A New Approach for Analyzing Average Time Complexity of Population-Based Evolutionary Algorithms on Unimodal Problems

Tianshi Chen, Jun He, Guoliang Chen (Guest editor), Xin Yao

Research output: Contribution to journalArticlepeer-review

58 Citations (SciVal)

Abstract

In the past decades, many theoretical results related to the time complexity of evolutionary algorithms (EAs) on different problems are obtained. However, there is not any general and easy-to-apply approach designed particularly for population-based EAs on unimodal problems. In this paper, we first generalize the concept of the takeover time to EAs with mutation, then we utilize the generalized takeover time to obtain the mean first hitting time of EAs and, thus, propose a general approach for analyzing EAs on unimodal problems. As examples, we consider the so-called (N + N) EAs and we show that, on two well-known unimodal problems, leadingones and onemax , the EAs with the bitwise mutation and two commonly used selection schemes both need O(nlnn + n 2/N) and O(n lnlnn + nlnn/N) generations to find the global optimum, respectively. Except for the new results above, our approach can also be applied directly for obtaining results for some population-based EAs on some other unimodal problems. Moreover, we also discuss when the general approach is valid to provide us tight bounds of the mean first hitting times and when our approach should be combined with problem-specific knowledge to get the tight bounds. It is the first time a general idea for analyzing population-based EAs on unimodal problems is discussed theoretically.
Original languageEnglish
Pages (from-to)1092-1106
JournalIEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics)
Volume39
Issue number5
DOIs
Publication statusPublished - 01 Oct 2009

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