On the effect of populations in evolutionary multi-objective optimisation.
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.
Giel
Oliver
O
Fakultät für Informatik, Technische Universität Dortmund, Germany. Oliver.Giel@cs.uni-dortmund.de
Lehre
Per Kristian
PK
D000465:Algorithms; D005075:Biological Evolution; D005828:Genetics, Population; D015233:Models, Statistical; D011157:Population Dynamics