GAAP. genetic algorithm with auxiliary populations applied to continuous optimization problems

Genetic algorithms have been used successfully to solve continuous optimization problems. However, an early convergence to low-quality solutions is one of the most common difficulties encountered when using these strategies. In this paper, a method that combines multiple auxiliary populations with t...

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Bibliographic Details
Published inProceedings of the ITI 2012 34th International Conference on Information Technology Interfaces pp. 411 - 416
Main Authors Corbalan, L., Hasperue, W., Lanzarini, L.
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.06.2012
Subjects
auxiliary populations
Biological cells
continuous optimization
Convergence
early convergence
Equations
exploitation
exploration
local search
Mathematical model
Optimization
Real coded genetic algorithms
Sociology
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Summary:Genetic algorithms have been used successfully to solve continuous optimization problems. However, an early convergence to low-quality solutions is one of the most common difficulties encountered when using these strategies. In this paper, a method that combines multiple auxiliary populations with the main population of the algorithm is proposed. The role of the auxiliary populations is dual: to prevent or hinder the early convergence to local suboptimal solutions, and to provide a local search mechanism for a greater exploitation of the most promising regions within the search space.
ISBN:1467316296
9781467316293
ISSN:1334-2762
DOI:10.2498/iti.2012.0382