Contrasting termination criteria for genetic algorithms

To find a good termination criterion for genetic algorithms is a difficult and frequently ignored task. In most instances the practitioner stops the algorittm after a predefined number of generations or function evaluations. How this number is established? This stop criteria assume a user's kno...

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Autores principales: Bermúdez, Carlos, Alfonso, Hugo, Gallard, Raúl Hector
Formato: Objeto de conferencia
Lenguaje:Español
Publicado: 1999
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Acceso en línea:http://sedici.unlp.edu.ar/handle/10915/22218
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id I19-R120-10915-22218
record_format dspace
institution Universidad Nacional de La Plata
institution_str I-19
repository_str R-120
collection SEDICI (UNLP)
language Español
topic Ciencias Informáticas
ARTIFICIAL INTELLIGENCE
Algorithms
genetic algorithms
contrasting termination criteria
spellingShingle Ciencias Informáticas
ARTIFICIAL INTELLIGENCE
Algorithms
genetic algorithms
contrasting termination criteria
Bermúdez, Carlos
Alfonso, Hugo
Gallard, Raúl Hector
Contrasting termination criteria for genetic algorithms
topic_facet Ciencias Informáticas
ARTIFICIAL INTELLIGENCE
Algorithms
genetic algorithms
contrasting termination criteria
description To find a good termination criterion for genetic algorithms is a difficult and frequently ignored task. In most instances the practitioner stops the algorittm after a predefined number of generations or function evaluations. How this number is established? This stop criteria assume a user's knowledge on the characteristic of the function, which influence the length of the search. But usually it is difficult to say a priori that the total number of generations should be a detemined one. ConsequentIy this approach can involve a waste of computational resources, because the genetic algorithm could stagnate at some local or global optimum and no further improvement is achieved in that condition. This presentation discusses perfomance results on evolutionary algorithms optimizing four highly multimodal functions (Michalewicz's F1 and F2, Branin's Rcos, Griewank's). The genotypic and phenotypic approaches were implemented using the Grefenstette's bias b and the stability of mean population fitness as measures of convergence, respectively. Quality of results and speed of convergence are the main perfomance variables contrasted.
format Objeto de conferencia
Objeto de conferencia
author Bermúdez, Carlos
Alfonso, Hugo
Gallard, Raúl Hector
author_facet Bermúdez, Carlos
Alfonso, Hugo
Gallard, Raúl Hector
author_sort Bermúdez, Carlos
title Contrasting termination criteria for genetic algorithms
title_short Contrasting termination criteria for genetic algorithms
title_full Contrasting termination criteria for genetic algorithms
title_fullStr Contrasting termination criteria for genetic algorithms
title_full_unstemmed Contrasting termination criteria for genetic algorithms
title_sort contrasting termination criteria for genetic algorithms
publishDate 1999
url http://sedici.unlp.edu.ar/handle/10915/22218
work_keys_str_mv AT bermudezcarlos contrastingterminationcriteriaforgeneticalgorithms
AT alfonsohugo contrastingterminationcriteriaforgeneticalgorithms
AT gallardraulhector contrastingterminationcriteriaforgeneticalgorithms
bdutipo_str Repositorios
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