Racing Multi-Objective Selection Probabilities

Gaétan Marceau 1 Marc Schoenauer 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 : In the context of Noisy Multi-Objective Optimization, dealing with uncertainties requires the decision maker to define some preferences about how to handle them, through some statistics (e.g., mean, median) to be used to evaluate the solution qualities, and define the corresponding Pareto set. Approximating these statistics requires repeated samplings of the population, drastically increasing the overall computational cost. To tackle this issue, this paper proposes to use some Hoeffding race to directly estimate the probability of each individual to be selected using a minimum number of samples, dynamically assigning the estimation budget during the selection step. The proposed racing approach is validated against static budget approaches with NSGA-II on noisy versions of the ZDT benchmark functions.
Type de document :
Autre publication
Extended pre-print of PPSN 2014 paper. 2014
Liste complète des métadonnées

https://hal.inria.fr/hal-01002854
Contributeur : Marc Schoenauer <>
Soumis le : vendredi 6 juin 2014 - 19:40:52
Dernière modification le : jeudi 5 avril 2018 - 12:30:12

Identifiants

  • HAL Id : hal-01002854, version 1

Collections

Citation

Gaétan Marceau, Marc Schoenauer. Racing Multi-Objective Selection Probabilities. Extended pre-print of PPSN 2014 paper. 2014. 〈hal-01002854〉

Partager

Métriques

Consultations de la notice

266