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: Stark, Natalia, Minetti, Gabriela F., Salto, Carolina
Formato: Objeto de conferencia
Lenguaje:Inglés
Publicado: 2012
Materias:
Ciencias Informáticas
Adapting the Mutation Probability
Genetic Algorithms
Algorithms
Intelligent agents
Acceso en línea:http://sedici.unlp.edu.ar/handle/10915/23593
Aporte de:
SEDICI (UNLP) de Universidad Nacional de La Plata
id I19-R120-10915-23593
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
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
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