Enhancing evolutionary algorithms through recombination and parallelism
Evolutionary computation (EC) has been recently recognized as a research field, which studies a new type of algorithms: Evolutionary Algorithms (EAs). These algorithms process populations of solutions as opposed to most traditional approaches which improve a single solution. All these algorithms sha...
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Formato: | Articulo |
Lenguaje: | Inglés |
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2001
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Acceso en línea: | http://sedici.unlp.edu.ar/handle/10915/9423 http://journal.info.unlp.edu.ar/wp-content/uploads/p9.pdf |
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I19-R120-10915-9423 |
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Universidad Nacional de La Plata |
institution_str |
I-19 |
repository_str |
R-120 |
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SEDICI (UNLP) |
language |
Inglés |
topic |
Ciencias Informáticas evolutionary algorithms multirecombination Parallel algorithms |
spellingShingle |
Ciencias Informáticas evolutionary algorithms multirecombination Parallel algorithms Gallard, Raúl Hector Esquivel, Susana Cecilia Enhancing evolutionary algorithms through recombination and parallelism |
topic_facet |
Ciencias Informáticas evolutionary algorithms multirecombination Parallel algorithms |
description |
Evolutionary computation (EC) has been recently recognized as a research field, which studies a new type of algorithms: Evolutionary Algorithms (EAs). These algorithms process populations of solutions as opposed to most traditional approaches which improve a single solution. All these algorithms share common features: reproduction, random variation, competition and selection of individuals. During our research it was evident that some components of EAs should be re-examined. Hence, specific topics such as multiple crossovers per couple and its enhancements, multiplicity of parents and crossovers and their application to single and multiple criteria optimization problems, adaptability, and parallel genetic algorithms, were proposed and investigated carefully. This paper show the most relevant and recent enhancements on recombination for a genetic-algorithm-based EA and migration control strategies for parallel genetic algorithms. Details of implementation and results are discussed. |
format |
Articulo Articulo |
author |
Gallard, Raúl Hector Esquivel, Susana Cecilia |
author_facet |
Gallard, Raúl Hector Esquivel, Susana Cecilia |
author_sort |
Gallard, Raúl Hector |
title |
Enhancing evolutionary algorithms through recombination and parallelism |
title_short |
Enhancing evolutionary algorithms through recombination and parallelism |
title_full |
Enhancing evolutionary algorithms through recombination and parallelism |
title_fullStr |
Enhancing evolutionary algorithms through recombination and parallelism |
title_full_unstemmed |
Enhancing evolutionary algorithms through recombination and parallelism |
title_sort |
enhancing evolutionary algorithms through recombination and parallelism |
publishDate |
2001 |
url |
http://sedici.unlp.edu.ar/handle/10915/9423 http://journal.info.unlp.edu.ar/wp-content/uploads/p9.pdf |
work_keys_str_mv |
AT gallardraulhector enhancingevolutionaryalgorithmsthroughrecombinationandparallelism AT esquivelsusanacecilia enhancingevolutionaryalgorithmsthroughrecombinationandparallelism |
bdutipo_str |
Repositorios |
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1764820491094196227 |