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Designing EDAs by using the elitist convergent EDA concept and the boltzmann distribution

Authors:

Sergio Ivvan Valdez,

Arturo Hernandez-Aguirre,

Salvador Botello-RiondaAuthors Info & Claims

GECCO '08: Proceedings of the 10th annual conference on Genetic and evolutionary computation

Pages 477 - 478

https://doi.org/10.1145/1389095.1389186

Published: 12 July 2008 Publication History

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Abstract

This paper presents a theoretical definition for designing EDAs called Elitist Convergent Estimation of Distribution Algorithm (ECEDA), and a practical implementation: the Boltzmann Univariate Marginal Distribution Algorithm (BUMDA). This proposal computes a Gaussian model which approximates a Boltzmann distribution via the minimization of the Kullback Leibler divergence. The resulting approach needs only one parameter: the population size. A set of problems is presented to show advantages and comparative performance of this approach with state of the art continuous EDAs.

References

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De Anda-Suarez JCarpio-Valadez JPuga-Soberanese HCalzada-Ledesma VRojas-Dominguez AJeyakumar SEspinal A(2019)Symmetric-Approximation Energy-Based Estimation of Distribution (SEED): A Continuous Optimization AlgorithmIEEE Access10.1109/ACCESS.2019.29481997(154859-154871)Online publication date: 2019
https://doi.org/10.1109/ACCESS.2019.2948199

Index Terms

Designing EDAs by using the elitist convergent EDA concept and the boltzmann distribution
1. Computing methodologies
  1. Artificial intelligence

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GECCO '08: Proceedings of the 10th annual conference on Genetic and evolutionary computation

July 2008

1814 pages

ISBN:9781605581309

DOI:10.1145/1389095

Conference Chair:
Conor Ryan
University of Limerick, Ireland
,
Editor:
Maarten Keijzer
Chordiant Software International B.V., The Netherlands

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Published: 12 July 2008

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GECCO08: Genetic and Evolutionary Computation Conference

July 12 - 16, 2008

GA, Atlanta, USA

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De Anda-Suarez JCarpio-Valadez JPuga-Soberanese HCalzada-Ledesma VRojas-Dominguez AJeyakumar SEspinal A(2019)Symmetric-Approximation Energy-Based Estimation of Distribution (SEED): A Continuous Optimization AlgorithmIEEE Access10.1109/ACCESS.2019.29481997(154859-154871)Online publication date: 2019
https://doi.org/10.1109/ACCESS.2019.2948199

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A Boltzmann based estimation of distribution algorithm

On the convergence of an estimation of distribution algorithm based on linkage discovery and factorization

An Estimation of Distribution Algorithm based on the Natural Gradient and the Boltzmann Distribution

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