On the effect of populations in evolutionary multi-objective optimization
Oliver Giel, Per Kristian Lehre · 2006
Multi-objective evolutionary algorithms (MOEAs) have become increasingly popular as multi-objective problem solving techniques. Most studies of MOEAs are empirical. Only recently, a few theoretical results have appeared. It is acknowledged that more theoretical research is needed. An important open problem is to understand the role of populations in MOEAs. We present a simple bi-objective problem which emphasizes when populations are needed. Rigorous runtime analysis point out an exponential runtime gap between a population-based algorithm (SEMO) and several single individual-based algorithms on this problem. This means that among the algorithms considered, only the populationbased MOEA is successful and all other algorithms fail.