Comparison-Based Complexity of Multiobjective Optimization

Olivier Teytaud 1, 2
1 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 : Several comparison-based complexity results have been published recently, including multi-objective optimization. H owever, these results are, in the multiobjective case, quite pessimistic, due to the huge family of fitness function s considered. Combining assumptions on fitness functions and traditional comparison-based assumptions, we get more r ealistic bounds emphasizing the importance of reducing the number of conflicting objectives for reducing the runtime of multiobjective optimization. The approach can in particular predict lower bounds on the computation time, depend ing on the type of requested convergence: pointwise, or to the whole Pareto set. Also, a new (untested yet) algorith m is proposed for approximating the whole Pareto set.
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Communication dans un congrès
ACM. GECCO 2011, 2011, Dublin, Ireland. ACM, pp.801-806, 2011, Proceedings of GECCO 2011
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Contributeur : Olivier Teytaud <>
Soumis le : samedi 9 février 2013 - 13:29:04
Dernière modification le : jeudi 5 avril 2018 - 12:30:12
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Olivier Teytaud. Comparison-Based Complexity of Multiobjective Optimization. ACM. GECCO 2011, 2011, Dublin, Ireland. ACM, pp.801-806, 2011, Proceedings of GECCO 2011. 〈hal-00786635〉

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