Enhancing evolutionary algorithms through recombination and parallelism

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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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Autores principales: Gallard, Raúl Hector, Esquivel, Susana Cecilia
Formato: Objeto de conferencia
Lenguaje:Inglés
Publicado: 2000
Materias:
Ciencias Informáticas
evolutionary algorithms
multirecombination
strategies for migration control
Acceso en línea:http://sedici.unlp.edu.ar/handle/10915/23410
Aporte de:
SEDICI (UNLP) de Universidad Nacional de La Plata
id I19-R120-10915-23410
record_format dspace
institution Universidad Nacional de La Plata
institution_str I-19
repository_str R-120
collection SEDICI (UNLP)
language Inglés
topic Ciencias Informáticas
evolutionary algorithms
multirecombination
strategies for migration control
spellingShingle Ciencias Informáticas
evolutionary algorithms
multirecombination
strategies for migration control
Gallard, Raúl Hector
Esquivel, Susana Cecilia
Enhancing evolutionary algorithms through recombination and parallelism
topic_facet Ciencias Informáticas
evolutionary algorithms
multirecombination
strategies for migration control
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 Objeto de conferencia
Objeto de conferencia
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 2000
url http://sedici.unlp.edu.ar/handle/10915/23410
work_keys_str_mv AT gallardraulhector enhancingevolutionaryalgorithmsthroughrecombinationandparallelism
AT esquivelsusanacecilia enhancingevolutionaryalgorithmsthroughrecombinationandparallelism
bdutipo_str Repositorios
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