Comparison of Different Approaches for Adapting Mutation Probabilities 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. In this paper we compare three different adaptive algorithms...

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Autores principales: Stark, Natalia, Minetti, Gabriela F., Salto, Carolina
Formato: Objeto de conferencia
Lenguaje:Inglés
Publicado: 2016
Materias:
Ciencias Informáticas
adaptive algorithms
mutation probability
Algoritmos
Acceso en línea:http://sedici.unlp.edu.ar/handle/10915/55739
Aporte de:
SEDICI (UNLP) de Universidad Nacional de La Plata
id I19-R120-10915-55739
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
adaptive algorithms
mutation probability
Algoritmos
spellingShingle Ciencias Informáticas
adaptive algorithms
mutation probability
Algoritmos
Stark, Natalia
Minetti, Gabriela F.
Salto, Carolina
Comparison of Different Approaches for Adapting Mutation Probabilities in Genetic Algorithms
topic_facet Ciencias Informáticas
adaptive algorithms
mutation probability
Algoritmos
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. In this paper we compare three different adaptive algorithms that include strategies to modify the mutation probability without external control. One adaptive strategy uses the genetic diversity present in the population to update the mutation probability. Other strategy is based on the ideas of reinforcement learning and the last one varies the probabilities of mutation depending on the fitness values of the solution. All these strategies eliminate a very expensive computational phase related to the pre-tuning of the algorithmic parameters. The empirical comparisons show that if the genetic algorithm uses the genetic diversity, as the strategy for adapting the mutation probability outperforms the other two strategies.
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 Comparison of Different Approaches for Adapting Mutation Probabilities in Genetic Algorithms
title_short Comparison of Different Approaches for Adapting Mutation Probabilities in Genetic Algorithms
title_full Comparison of Different Approaches for Adapting Mutation Probabilities in Genetic Algorithms
title_fullStr Comparison of Different Approaches for Adapting Mutation Probabilities in Genetic Algorithms
title_full_unstemmed Comparison of Different Approaches for Adapting Mutation Probabilities in Genetic Algorithms
title_sort comparison of different approaches for adapting mutation probabilities in genetic algorithms
publishDate 2016
url http://sedici.unlp.edu.ar/handle/10915/55739
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