Theory and practice of cellular UMDA for discrete optimization

Authors:
E. Alba;J. Madera;B. Dorronsoro;A. Ochoa;M. Soto
Affiliations:
Department of Computer Science, University of Málaga, Spain;Department of Computing, Camagüey University, Cuba;Department of Computer Science, University of Málaga, Spain;The Institute of Cybernetics, Mathematics and Physics, La Habana, Cuba;The Institute of Cybernetics, Mathematics and Physics, La Habana, Cuba
Venue:
PPSN'06 Proceedings of the 9th international conference on Parallel Problem Solving from Nature
Year:
2006

Citing 10
Cited 1

Estimation of Distribution Algorithms: A New Tool for Evolutionary Computation

Estimation of Distribution Algorithms: A New Tool for Evolutionary Computation
Improving flexibility and efficiency by adding parallelism to genetic algorithms

Statistics and Computing
Fine-Grained Parallel Genetic Algorithms

Proceedings of the 3rd International Conference on Genetic Algorithms
Structure and Performance of Fine-Grain Parallelism in Genetic Search

Proceedings of the 5th International Conference on Genetic Algorithms
From Recombination of Genes to the Estimation of Distributions I. Binary Parameters

PPSN IV Proceedings of the 4th International Conference on Parallel Problem Solving from Nature
Feature Subset Selection By Estimation Of Distribution Algorithms

GECCO '02 Proceedings of the Genetic and Evolutionary Computation Conference
The science of breeding and its application to the breeder genetic algorithm (bga)

Evolutionary Computation
Paper: The parallel genetic algorithm as function optimizer

Parallel Computing
Selection intensity in asynchronous cellular evolutionary algorithms

GECCO'03 Proceedings of the 2003 international conference on Genetic and evolutionary computation: PartI
The exploration/exploitation tradeoff in dynamic cellular genetic algorithms

IEEE Transactions on Evolutionary Computation

Differential evolution algorithms with cellular populations

PPSN'10 Proceedings of the 11th international conference on Parallel problem solving from nature: Part II

Quantified Score

Hi-index	0.00

Visualization

Abstract

A new class of estimation of distribution algorithms (EDAs), known as cellular EDAs (cEDAs), has recently emerged. In these algorithms, the population is decentralized by partitioning it into many small collaborating subpopulations, arranged in a toroidal grid, and interacting only with its neighboring subpopulations. In this work, we study the simplest cEDA —the cellular univariate marginal distribution algorithm (cUMDA). In an attempt to explain its behaviour, we extend the well known takeover time analysis usually applied to other evolutionary algorithms to the field of EDAs. We also give in this work empirical arguments in favor of using the cUMDAs instead of its centralized equivalent.