Improving evolutionary algorithms performance by extending incest prevention

Provision of population diversity is one of the main goals to avoid premature convergence in Evolutionary Algorithms (EAs). In this way the risk of being trapped in local optima is minimised. Eshelman and Shaffer [4] attempted to maintain population diversity by using diverse strategies focusing on...

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Autores principales: Alfonso, Hugo, Cesan, P., Fernandez, Natalia, Minetti, Gabriela F., Salto, Carolina, Velazco, L., Gallard, Raúl Hector
Formato: Objeto de conferencia
Lenguaje:Inglés
Publicado: 1998
Materias:
Acceso en línea:http://sedici.unlp.edu.ar/handle/10915/24823
Aporte de:
id I19-R120-10915-24823
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
Informática
evolutionary algorithms
genetic diversity
premature convergence
selection mechanisms
incest prevention
Biology and genetics
Algorithms
Combinatorial algorithms
Selection process
spellingShingle Ciencias Informáticas
Informática
evolutionary algorithms
genetic diversity
premature convergence
selection mechanisms
incest prevention
Biology and genetics
Algorithms
Combinatorial algorithms
Selection process
Alfonso, Hugo
Cesan, P.
Fernandez, Natalia
Minetti, Gabriela F.
Salto, Carolina
Velazco, L.
Gallard, Raúl Hector
Improving evolutionary algorithms performance by extending incest prevention
topic_facet Ciencias Informáticas
Informática
evolutionary algorithms
genetic diversity
premature convergence
selection mechanisms
incest prevention
Biology and genetics
Algorithms
Combinatorial algorithms
Selection process
description Provision of population diversity is one of the main goals to avoid premature convergence in Evolutionary Algorithms (EAs). In this way the risk of being trapped in local optima is minimised. Eshelman and Shaffer [4] attempted to maintain population diversity by using diverse strategies focusing on mating, recombination and replacement. One of their approaches, called incest prevention, avoided mating of pairs showing similarities based on the parent’s hamming distance. Conventional selection mechanisms does not consider if the members of the new population have common ancestors and consequently due to a finite fixed population size, a loss of genetic diversity can frequently arise. This paper shows an extended approach of incest prevention by maintaining information about ancestors within the chromosome and modifying the selection for reproduction in order to impede mating of individuals belonging to the same “family”, for a predefined number of generations. This novel approach was tested on a set of multimodal functions. Description of experiments and analyses of improved results are also shown.
format Objeto de conferencia
Objeto de conferencia
author Alfonso, Hugo
Cesan, P.
Fernandez, Natalia
Minetti, Gabriela F.
Salto, Carolina
Velazco, L.
Gallard, Raúl Hector
author_facet Alfonso, Hugo
Cesan, P.
Fernandez, Natalia
Minetti, Gabriela F.
Salto, Carolina
Velazco, L.
Gallard, Raúl Hector
author_sort Alfonso, Hugo
title Improving evolutionary algorithms performance by extending incest prevention
title_short Improving evolutionary algorithms performance by extending incest prevention
title_full Improving evolutionary algorithms performance by extending incest prevention
title_fullStr Improving evolutionary algorithms performance by extending incest prevention
title_full_unstemmed Improving evolutionary algorithms performance by extending incest prevention
title_sort improving evolutionary algorithms performance by extending incest prevention
publishDate 1998
url http://sedici.unlp.edu.ar/handle/10915/24823
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