On the Effect of Populations in Evolutionary Multi-Objective Optimisation
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Colleges, School and Institutes
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.
|Number of pages||22|
|Publication status||Published - 1 Oct 2010|
- runtime analysis, multi-objective optimisation, Evolutionary algorithms