On the benefits of Laplace samples in solving a rare event problem using cross-entropy method (Articolo in rivista)

Type

• Articolo in rivista (Classe)
• Prodotto della ricerca (Classe)

Label

• On the benefits of Laplace samples in solving a rare event problem using cross-entropy method (Articolo in rivista) (literal)

Anno

• 2013-01-01T00:00:00+01:00 (literal)

Alternative label

• S. Easter Selvan M.S.P. Subathra, A. Hepzibah Christinal, U. Amato (2013)
  On the benefits of Laplace samples in solving a rare event problem using cross-entropy method
  in Applied mathematics and computation
  (literal)

Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#autori

• S. Easter Selvan M.S.P. Subathra, A. Hepzibah Christinal, U. Amato (literal)

Pagina inizio

• 843 (literal)

Pagina fine

• 859 (literal)

Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#numeroVolume

• 225 (literal)

Rivista

• Applied mathematics and computation (Rivista)

Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#pagineTotali

• 17 (literal)

Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#affiliazioni

• E. Selvan: Department of Mathematical Engineering, Université catholique de Louvain, 1348 Louvain-la-Neuve, Belgium M.S.P. Subathra, A. Hepzibah Christinal: Karunya University, Coimbatore 641114, Tamil Nadu, India (literal)

Titolo

• On the benefits of Laplace samples in solving a rare event problem using cross-entropy method (literal)

Abstract

• The convergence quality of the cross-entropy (CE) optimizer relies critically on the mechanism meant for randomly generating data samples, in agreement with the inference drawn in the earlier works--the fast simulated annealing (FSA) and fast evolutionary programming (FEP). Since tracing a near-global-optimum embedded on a nonconvex search space can be viewed as a rare event problem, a CE algorithm constructed using a longtailed distribution is intuitively attractive for effectively exploring the optimization landscape. Based on this supposition, a set of CE algorithms employing the Cauchy, logistic and Laplace distributions are experimentally validated in a wide range of optimization functions, which are shifted, rotated, expanded and/or composed, characterized by convex, unimodal, discontinuous, noisy and multimodal fitness landscapes. The Laplace distribution has been demonstrated to be more suitable for the CE optimization, since the samples drawn have jump-lengths long enough to elude local optima and short enough to preserve sufficient candidates in the global optimum neighborhood. Besides, a theoretical analysis has been carried out to understand the following: (i) benefits offered by the long-tailed distributions towards evasion of local optima; (ii) link between the variation in scale parameter estimate and the probability of producing candidate solutions arbitrarily close to the global optimum. (literal)

Prodotto di

• Sviluppo di metodi matematici e statistici e del relativo software orientato al grid computing (ICT.P11.007.001) (Modulo)
• Istituto per le applicazioni del calcolo \"Mauro Picone\" (IAC) (Istituto)

Autore CNR

• UMBERTO AMATO (Unità di personale interno)

Incoming links:

Prodotto

• Istituto per le applicazioni del calcolo \"Mauro Picone\" (IAC) (Istituto)
• Sviluppo di metodi matematici e statistici e del relativo software orientato al grid computing (ICT.P11.007.001) (Modulo)

Autore CNR di

• UMBERTO AMATO (Unità di personale interno)

Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#rivistaDi

• Applied mathematics and computation (Rivista)