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2009
Springer

On Using Populations of Sets in Multiobjective Optimization

8 years 10 months ago
On Using Populations of Sets in Multiobjective Optimization
Abstract. Most existing evolutionary approaches to multiobjective optimization aim at finding an appropriate set of compromise solutions, ideally a subset of the Pareto-optimal set. That means they are solving a set problem where the search space consists of all possible solution sets. Taking this perspective, multiobjective evolutionary algorithms can be regarded as hill-climbers on solution sets: the population is one element of the set search space and selection as well as variation implement a specific type of set mutation operator. Therefore, one may ask whether a ‘real’ evolutionary algorithm on solution sets can have advantages over the classical single-population approach. This paper investigates this issue; it presents a multi-population multiobjective optimization framework and demonstrates its usefulness on several test problems and a sensor network application. 1 Motivation Most multiobjective evolutionary algorithms (MOEAs) proposed in the literature are designed tow...
Johannes Bader, Dimo Brockhoff, Samuel Welten, Eck
Added 24 Jul 2010
Updated 24 Jul 2010
Type Conference
Year 2009
Where EMO
Authors Johannes Bader, Dimo Brockhoff, Samuel Welten, Eckart Zitzler
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