Abstract
The computational time complexity is an important topic in the theory of evolutionary algorithms (EAs). This paper reports some new results on the average time complexity of EAs. Based on drift analysis, some useful drift conditions for deriving the time complexity of EAs are studied, including conditions under which an EA will take no more than polynomial time (in problem size) to solve a problem and conditions under which an EA will take at least exponential time (in problem size) to solve a problem. The paper first presents the general results, and then uses several problems as examples to illustrate how these general results can be applied to concrete problems in analyzing the average time complexity of EAs. While previous work only considered (1+1) EAs without any crossover, the EAs considered in this paper are fairly general, which use a finite population, crossover, mutation, and selection.
| Original language | English |
|---|---|
| Pages (from-to) | 57-85 |
| Number of pages | 29 |
| Journal | Artificial Intelligence |
| Volume | 127 |
| Issue number | 1 |
| DOIs | |
| Publication status | Published - Mar 2001 |
| Externally published | Yes |
Funding
This work was partially supported by the State Key Laboratory of Software Engineering at Wuhan University, National Nature Science Foundation of China, the Australian Research Council, and the School of Computer Science, the University of Birmingham.