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Evolutionary Computation

Fall 2010, Vol. 18, No. 3, Pages 335-356
(doi: 10.1162/EVCO_a_00013)
© 2010 by the Massachusetts Institute of Technology
On the Effect of Populations in Evolutionary Multi-Objective Optimisation
Article PDF (246.11 KB)

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.