Conference paper · Journal article
On the Effect of Populations in Evolutionary Multi-Objective Optimisation
Multi-objective evolutionary algorithms (MOEAs) have become increasingly popular as multi-objective problem solving techniques. An important open problem is to understand the role of populations in MOEAs. We present two simple bi-objective problems which emphasise when populations are needed. Rigorous runtime analysis points out an exponential runtime gap between the population-based algorithm Simple Evolutionary Multi-objective Optimiser (SEMO) and several single individual-based algorithms on this problem.
This means that among the algorithms considered, only the population-based MOEA is successful and all other algorithms fail.
| Language: | English |
|---|---|
| Publisher: | ACM |
| Year: | 2010 |
| Pages: | 335-356 |
| ISSN: | 15309304 and 10636560 |
| Types: | Conference paper and Journal article |
| DOI: | 10.1145/1143997.1144114 |
Markov-chain Monte Carlo methods Mathematics of computing Probabilistic algorithms Probabilistic reasoning algorithms Probability and statistics Randomness, geometry and discrete structures Sequential Monte Carlo methods Theory of computation evolutionary algorithms multi-objective optimization runtime analysis
