On the effect of populations in evolutionary multi-objective optimization
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Date
2007-06-04T16:20:03Z
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Abstract
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.