| Population-based continuous optimization, probabilistic modelling and mean shift. | |
| | |
MedLine Citation:
|
PMID: 15901425 Owner: NLM Status: MEDLINE |
Abstract/OtherAbstract:
|
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. |
| | |
Authors:
|
Marcus Gallagher; Marcus Frean |
Related Documents
:
|
18255395 - Recognizing multiple overlapping objects in image: an optimal formulation. 11108475 - A simple iterative approach to parameter optimization. 11694015 - Application of a mixed optimization strategy in the design of a pharmaceutical solid fo... 20365255 - Estimating structure of multivariate systems with genetic algorithms for nonlinear pred... 11144585 - Interrelations among distortion-product phase-gradient delays: their connection to scal... 22044445 - Validity of assessing inhibitor development in haemophilia pups using registry data: th... |
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 |
Affiliation:
|
School of Information Technology and Electrical Engineering, University of Queensland, Brisbane, QLD 4072, Australia. marcusg@itee.uq.edu.au |
Export Citation:
|
APA/MLA Format Download EndNote Download BibTex |
| MeSH Terms | |
Descriptor/Qualifier:
|
Algorithms Cluster Analysis Computational Biology / methods* Evolution* Evolution, Molecular Models, Statistical Models, Theoretical Normal Distribution Population Dynamics Probability |
From MEDLINE®/PubMed®, a database of the U.S. National Library of Medicine
Previous Document: The estimation of distributions and the minimum relative entropy principle.
Next Document: Globally multimodal problem optimization via an estimation of distribution algorithm based on unsupe...