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A population-based cross-entropy method with dynamic sample allocation

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

1 Scopus citations

Abstract

This paper generalizes the cross-entropy (CE) method to a population-based setting, where a population of probabilistic models is maintained/updated and subsequently propagated from generation to generation. One of the key questions in the proposed approach is how to efficiently distribute a given sample budget among different models in a population to maximize algorithm performance. We formulate this problem as a Markov decision process (MDP) model and derive an efficient dynamic sample allocation scheme to adaptively allocate computational resources. We carry out numerical studies to illustrate the method and compare its performance with existing procedures.

Original languageEnglish
Title of host publicationProceedings of the 47th IEEE Conference on Decision and Control, CDC 2008
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages2426-2431
Number of pages6
ISBN (Print)9781424431243
DOIs
StatePublished - 2008
Event47th IEEE Conference on Decision and Control, CDC 2008 - Cancun, Mexico
Duration: Dec 9 2008Dec 11 2008

Publication series

NameProceedings of the IEEE Conference on Decision and Control

Conference

Conference47th IEEE Conference on Decision and Control, CDC 2008
Country/TerritoryMexico
CityCancun
Period12/9/0812/11/08

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