Comparison of the MATSuMoTo Library for ...
Type de document :
Autre communication scientifique (congrès sans actes - poster - séminaire...): Communication dans un congrès avec actes
Titre :
Comparison of the MATSuMoTo Library for Expensive Optimization on the Noiseless Black-Box Optimization Benchmarking Testbed
Auteur(s) :
Titre de la manifestation scientifique :
Congress on Evolutionary Computation (CEC 2015)
Ville :
Sendai
Pays :
Japon
Date de début de la manifestation scientifique :
2015-05-25
Discipline(s) HAL :
Mathématiques [math]/Optimisation et contrôle [math.OC]
Informatique [cs]/Recherche d'information [cs.IR]
Informatique [cs]/Recherche d'information [cs.IR]
Résumé en anglais : [en]
Numerical black-box optimization problems occur frequently in engineering design, medical applications, finance, and many other areas of our society's interest. Often, those problems have expensive-to-calculate objective ...
Lire la suite >Numerical black-box optimization problems occur frequently in engineering design, medical applications, finance, and many other areas of our society's interest. Often, those problems have expensive-to-calculate objective functions for example if the solution evaluation is based on numerical simulations. Starting with the seminal paper of Jones et al. on Efficient Global Optimization (EGO), several algorithms tailored towards expensive numerical black-box problems have been proposed. The recent MATLAB toolbox MATSuMoTo (short for MATLAB Surrogate Model Toolbox) is the focus of this paper and is benchmarked within the Black-box Optimization Benchmarking framework BBOB. A comparison with other already previously benchmarked algorithms for expensive numerical black-box optimization with the default setting of MATSuMoTo highlights the strengths and weaknesses of MATSuMoTo's cubic radial basis functions surrogate model in combination with a Latin Hypercube initial design in the range of 50 times dimension many function evaluations.Lire moins >
Lire la suite >Numerical black-box optimization problems occur frequently in engineering design, medical applications, finance, and many other areas of our society's interest. Often, those problems have expensive-to-calculate objective functions for example if the solution evaluation is based on numerical simulations. Starting with the seminal paper of Jones et al. on Efficient Global Optimization (EGO), several algorithms tailored towards expensive numerical black-box problems have been proposed. The recent MATLAB toolbox MATSuMoTo (short for MATLAB Surrogate Model Toolbox) is the focus of this paper and is benchmarked within the Black-box Optimization Benchmarking framework BBOB. A comparison with other already previously benchmarked algorithms for expensive numerical black-box optimization with the default setting of MATSuMoTo highlights the strengths and weaknesses of MATSuMoTo's cubic radial basis functions surrogate model in combination with a Latin Hypercube initial design in the range of 50 times dimension many function evaluations.Lire moins >
Langue :
Anglais
Comité de lecture :
Oui
Audience :
Internationale
Vulgarisation :
Non
Projet ANR :
Collections :
Source :
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- matsumotoCECpaper-authorversion.pdf
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