A new strategy for adapting the mutation probability in genetic algorithms
Traditionally in Genetic Algorithms, the mutation probability parameter maintains a constant value during the search. However, an important difficulty is to determine a priori which probability value is the best suited for a given problem. Besides, there is a growing demand for up-to-date optimizati...
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Autores principales: | , , |
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Formato: | Objeto de conferencia |
Lenguaje: | Inglés |
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2012
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Acceso en línea: | http://sedici.unlp.edu.ar/handle/10915/23593 |
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I19-R120-10915-23593 |
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institution |
Universidad Nacional de La Plata |
institution_str |
I-19 |
repository_str |
R-120 |
collection |
SEDICI (UNLP) |
language |
Inglés |
topic |
Ciencias Informáticas Adapting the Mutation Probability Genetic Algorithms Algorithms Intelligent agents |
spellingShingle |
Ciencias Informáticas Adapting the Mutation Probability Genetic Algorithms Algorithms Intelligent agents Stark, Natalia Minetti, Gabriela F. Salto, Carolina A new strategy for adapting the mutation probability in genetic algorithms |
topic_facet |
Ciencias Informáticas Adapting the Mutation Probability Genetic Algorithms Algorithms Intelligent agents |
description |
Traditionally in Genetic Algorithms, the mutation probability parameter maintains a constant value during the search. However, an important difficulty is to determine a priori which probability value is the best suited for a given problem. Besides, there is a growing demand for up-to-date optimization software, applicable by a non-specialist within an industrial development environment. These issues encourage us to propose an adaptive evolutionary algorithm that includes a mechanism to modify the mutation probability without external control. This process of dynamic adaptation happens while the algorithm is searching for the problem solution. This eliminates a very expensive computational phase related to the pre-tuning of the algorithmic parameters. We compare the performance of our adaptive proposal against traditional genetic algorithms with fixed parameter values in a numerical way. The empirical comparisons, over a range of NK-Landscapes instances, show that a genetic algorithm incorporating a strategy for adapting the mutation probability outperforms the same algorithm using fixed mutation rates. |
format |
Objeto de conferencia Objeto de conferencia |
author |
Stark, Natalia Minetti, Gabriela F. Salto, Carolina |
author_facet |
Stark, Natalia Minetti, Gabriela F. Salto, Carolina |
author_sort |
Stark, Natalia |
title |
A new strategy for adapting the mutation probability in genetic algorithms |
title_short |
A new strategy for adapting the mutation probability in genetic algorithms |
title_full |
A new strategy for adapting the mutation probability in genetic algorithms |
title_fullStr |
A new strategy for adapting the mutation probability in genetic algorithms |
title_full_unstemmed |
A new strategy for adapting the mutation probability in genetic algorithms |
title_sort |
new strategy for adapting the mutation probability in genetic algorithms |
publishDate |
2012 |
url |
http://sedici.unlp.edu.ar/handle/10915/23593 |
work_keys_str_mv |
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bdutipo_str |
Repositorios |
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1764820466002821123 |