Combining incest prevention and multiplicity in evolutionary algorithms

Evolutionary Computation is an emergent field, which provides new heuristics to function optimization where traditional approaches make the problem computationally intractable. Exploration and exploitation of solution in the problem space are main issues affecting the performance of an evolutionary...

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Detalles Bibliográficos
Autores principales: Minetti, Gabriela F., Salto, Carolina, Alfonso, Hugo, Gallard, Raúl Hector
Formato: Objeto de conferencia
Lenguaje:Inglés
Publicado: 2001
Materias:
Ciencias Informáticas
Scheduling
Evolución
Algorithms
Genetic Algorithms
Multiple Crossovers
Multiple Parents
Incest Prevention
Cluster Allocation
Acceso en línea:http://sedici.unlp.edu.ar/handle/10915/23522
Aporte de:
SEDICI (UNLP) de Universidad Nacional de La Plata
id I19-R120-10915-23522
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
Scheduling
Evolución
Algorithms
Genetic Algorithms
Multiple Crossovers
Multiple Parents
Incest Prevention
Cluster Allocation
spellingShingle Ciencias Informáticas
Scheduling
Evolución
Algorithms
Genetic Algorithms
Multiple Crossovers
Multiple Parents
Incest Prevention
Cluster Allocation
Minetti, Gabriela F.
Salto, Carolina
Alfonso, Hugo
Gallard, Raúl Hector
Combining incest prevention and multiplicity in evolutionary algorithms
topic_facet Ciencias Informáticas
Scheduling
Evolución
Algorithms
Genetic Algorithms
Multiple Crossovers
Multiple Parents
Incest Prevention
Cluster Allocation
description Evolutionary Computation is an emergent field, which provides new heuristics to function optimization where traditional approaches make the problem computationally intractable. Exploration and exploitation of solution in the problem space are main issues affecting the performance of an evolutionary algorithm. Current enhancements attempt to balance exploitation and exploration to avoid premature convergence during the search process. Multiple parents multiple crossovers and incest prevention are three different techniques that when combined showed a substantial benefit: besides minimizing the risk of premature convergence, the final population is concentrated nearby the optimal solution. This behaviour is an important aid provided by the evolutionary process when applications require a set of alternative solutions to face system dynamics. This paper shows the design, implementation and partial performance results when incest prevention is combined with multiple crossovers on multiple parents for difficult multimodal optimization.
format Objeto de conferencia
Objeto de conferencia
author Minetti, Gabriela F.
Salto, Carolina
Alfonso, Hugo
Gallard, Raúl Hector
author_facet Minetti, Gabriela F.
Salto, Carolina
Alfonso, Hugo
Gallard, Raúl Hector
author_sort Minetti, Gabriela F.
title Combining incest prevention and multiplicity in evolutionary algorithms
title_short Combining incest prevention and multiplicity in evolutionary algorithms
title_full Combining incest prevention and multiplicity in evolutionary algorithms
title_fullStr Combining incest prevention and multiplicity in evolutionary algorithms
title_full_unstemmed Combining incest prevention and multiplicity in evolutionary algorithms
title_sort combining incest prevention and multiplicity in evolutionary algorithms
publishDate 2001
url http://sedici.unlp.edu.ar/handle/10915/23522
work_keys_str_mv AT minettigabrielaf combiningincestpreventionandmultiplicityinevolutionaryalgorithms
AT saltocarolina combiningincestpreventionandmultiplicityinevolutionaryalgorithms
AT alfonsohugo combiningincestpreventionandmultiplicityinevolutionaryalgorithms
AT gallardraulhector combiningincestpreventionandmultiplicityinevolutionaryalgorithms
bdutipo_str Repositorios
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