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| Authors: | Marco Laumanns, Lothar Thiele, Eckart Zitzler, Emo Welzl, Kalyanmoy Deb |
| Group: | Computer Engineering |
| Type: | Inproceedings |
| Title: | Running time analysis of multi-objective evolutionary algorithms on a simple discrete optimization problem |
| Year: | 2002 |
| Month: | September |
| Pub-Key: | LTZWD2002b |
| Book Titel: | Lecture Notes in Computer Science. Parallel Problem Solving From Nature (PPSN VII) |
| Volume: | 2439 |
| Pages: | 44-53 |
| Keywords: | EMO |
| Publisher: | Springer |
| Abstract: | For the first time, a running time analysis of population-based multi-objective evolutionary algorithms for a discrete optimization problem is given. To this end, we define a simple pseudo-Boolean bi-objective problem ($ extsc{Lotz}$: leading ones - trailing zeroes) and investigate time required to find the entire set of Pareto-optimal solutions. It is shown that different multi-objective generalizations of a (1+1) evolutionary algorithm (EA) as well as a simple population-based evolutionary multi-objective optimizer (SEMO) need on average at least $Theta(n^3)$ steps to optimize this function. We propose the fair evolutionary multi-objective optimizer (FEMO) and prove that this algorithm performs a black box optimization in $Theta(n^2 log n)$ function evaluations where $n$ is the number of binary decision variables. |
| Location: | Berlin |
| Resources: | [BibTeX] [Paper as PDF] |
