Document Detail

Population-based continuous optimization, probabilistic modelling and mean shift.
MedLine Citation:
PMID:  15901425     Owner:  NLM     Status:  MEDLINE    
Evolutionary algorithms perform optimization using a population of sample solution points. An interesting development has been to view population-based optimization as the process of evolving an explicit, probabilistic model of the search space. This paper investigates a formal basis for continuous, population-based optimization in terms of a stochastic gradient descent on the Kullback-Leibler divergence between the model probability density and the objective function, represented as an unknown density of assumed form. This leads to an update rule that is related and compared with previous theoretical work, a continuous version of the population-based incremental learning algorithm, and the generalized mean shift clustering framework. Experimental results are presented that demonstrate the dynamics of the new algorithm on a set of simple test problems.
Marcus Gallagher; Marcus Frean
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Publication Detail:
Type:  Journal Article    
Journal Detail:
Title:  Evolutionary computation     Volume:  13     ISSN:  1063-6560     ISO Abbreviation:  Evol Comput     Publication Date:  2005  
Date Detail:
Created Date:  2005-05-19     Completed Date:  2005-08-08     Revised Date:  -    
Medline Journal Info:
Nlm Unique ID:  9513581     Medline TA:  Evol Comput     Country:  United States    
Other Details:
Languages:  eng     Pagination:  29-42     Citation Subset:  IM    
School of Information Technology and Electrical Engineering, University of Queensland, Brisbane, QLD 4072, Australia.
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MeSH Terms
Cluster Analysis
Computational Biology / methods*
Evolution, Molecular
Models, Statistical
Models, Theoretical
Normal Distribution
Population Dynamics

From MEDLINE®/PubMed®, a database of the U.S. National Library of Medicine

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