Self-adaptation of parameters for MCPC in genetic algorithms

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As a new promising crossover method, multiple crossovers per couple (MCPC) deserves special attention in evolutionary computing field. Allowing multiple crossovers per couple on a selected pair of parents provided an extra benefit in processing time and similar quality of solutions when contrasted a...

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Autores principales: Esquivel, Susana Cecilia, Leiva, Héctor Ariel, Gallard, Raúl Hector
Formato: Objeto de conferencia
Lenguaje:Inglés
Publicado: 1998
Materias:
Ciencias Informáticas
Informática
Biology and genetics
Self-assessment
Selection process
genetic algorithms
self-adaptation
crossover
function optimization
Acceso en línea:http://sedici.unlp.edu.ar/handle/10915/24822
Aporte de:
SEDICI (UNLP) de Universidad Nacional de La Plata
id I19-R120-10915-24822
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
Biology and genetics
Self-assessment
Selection process
genetic algorithms
self-adaptation
crossover
function optimization
spellingShingle Ciencias Informáticas
Informática
Biology and genetics
Self-assessment
Selection process
genetic algorithms
self-adaptation
crossover
function optimization
Esquivel, Susana Cecilia
Leiva, Héctor Ariel
Gallard, Raúl Hector
Self-adaptation of parameters for MCPC in genetic algorithms
topic_facet Ciencias Informáticas
Informática
Biology and genetics
Self-assessment
Selection process
genetic algorithms
self-adaptation
crossover
function optimization
description As a new promising crossover method, multiple crossovers per couple (MCPC) deserves special attention in evolutionary computing field. Allowing multiple crossovers per couple on a selected pair of parents provided an extra benefit in processing time and similar quality of solutions when contrasted against the conventional single crossover per couple approach (SCPC). These results, were confirmed when optimising classic testing functions and harder (non-linear, non-separable) functions. Despite these benefits, due to a reinforcement of selective pressure, MCPC showed in some cases an undesirable premature convergence effect. In order to face this problem, the present paper attempts to control the number of crossovers, and offspring, allowed to the mating pair in a self-adaptive manner. Self-adaptation of parameters is a central feature of evolutionary strategies, another class of evolutionary algorithms, which simultaneously apply evolutionary principles on the search space of object variables and on strategy parameters. In other words, parameter values are also submitted to the evolutionary process. This approach can be also applied to genetic algorithms. In the case of MCPC, the number of crossovers allowed to a selected couple is a key parameter and consequently self-adaptation is achieved by adding to the chromosome structure "labels" describing the number of crossover allowed to each individual. Labels, which are bit strings, also undergo crossover and mutation and consequently evolve together with the individual. During the stages of the evolution process, it is expected that the algorithm will return the number of crossovers for which the current population exhibits a better behaviour. Descriptions of different self-adaptation methods used, experiments and some of the results obtained are shown.
format Objeto de conferencia
Objeto de conferencia
author Esquivel, Susana Cecilia
Leiva, Héctor Ariel
Gallard, Raúl Hector
author_facet Esquivel, Susana Cecilia
Leiva, Héctor Ariel
Gallard, Raúl Hector
author_sort Esquivel, Susana Cecilia
title Self-adaptation of parameters for MCPC in genetic algorithms
title_short Self-adaptation of parameters for MCPC in genetic algorithms
title_full Self-adaptation of parameters for MCPC in genetic algorithms
title_fullStr Self-adaptation of parameters for MCPC in genetic algorithms
title_full_unstemmed Self-adaptation of parameters for MCPC in genetic algorithms
title_sort self-adaptation of parameters for mcpc in genetic algorithms
publishDate 1998
url http://sedici.unlp.edu.ar/handle/10915/24822
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AT leivahectorariel selfadaptationofparametersformcpcingeneticalgorithms
AT gallardraulhector selfadaptationofparametersformcpcingeneticalgorithms
bdutipo_str Repositorios
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