期刊
EVOLUTIONARY COMPUTATION
卷 18, 期 3, 页码 335-356出版社
MIT PRESS
DOI: 10.1162/EVCO_a_00013
关键词
Evolutionary algorithms; multi-objective optimisation; runtime analysis
资金
- German Research Foundation (DFG) [SFB 531]
- EPSRC [EP/D052785/1]
- EPSRC [EP/D052785/1] Funding Source: UKRI
- Engineering and Physical Sciences Research Council [EP/D052785/1] Funding Source: researchfish
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.
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