On the Effect of Populations in Evolutionary Multi-Objective Optimisation

Oliver Giel, Per Kristian Lehre

    Research output: Contribution to journalJournal articleResearchpeer-review


    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
    JournalEvolutionary Computation
    Issue number3
    Pages (from-to)335-356
    Publication statusPublished - 2010


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