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On Using Populations of Sets in Multiobjective Optimization

Johannes Bader 1 Dimo Brockhoff 2 Samuel Welten 1 Eckart Zitzler 1 
2 TAO - Machine Learning and Optimisation
LRI - Laboratoire de Recherche en Informatique, UP11 - Université Paris-Sud - Paris 11, Inria Saclay - Ile de France, CNRS - Centre National de la Recherche Scientifique : UMR8623
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.
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Submitted on : Friday, November 12, 2010 - 7:02:53 PM
Last modification on : Saturday, June 25, 2022 - 10:02:04 PM
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  • HAL Id : inria-00535807, version 1


Johannes Bader, Dimo Brockhoff, Samuel Welten, Eckart Zitzler. On Using Populations of Sets in Multiobjective Optimization. Evolutionary Multi-Criterion Optimization (EMO 2009), Apr 2009, Nantes, France. pp.140-154. ⟨inria-00535807⟩



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