On the Effect of Populations in Evolutionary Multi-Objective Optimisation

O Giel, Per Lehre

Research output: Contribution to journalArticle

24 Citations (Scopus)


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.
Original languageEnglish
Pages (from-to)335-356
Number of pages22
JournalEvolutionary Computation
Issue number3
Publication statusPublished - 1 Oct 2010


  • runtime analysis
  • multi-objective optimisation
  • Evolutionary algorithms


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