Self adaptation of parameters for MCPC in genetic algorithms

Mostrar todas las versiones(2)

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...

Descripción completa

Guardado en:

Detalles Bibliográficos
Autores principales:	Esquivel, Susana Cecilia, Leiva, Héctor Ariel, Gallard, Raúl Hector
Formato:	Articulo
Lenguaje:	Inglés
Publicado:	2000
Materias:	Ciencias Informáticas genetic algorithms; self-adaptation; crossover; function optimisation Algoritmos evolutivos Optimización Informática
Acceso en línea:	http://sedici.unlp.edu.ar/handle/10915/9385 http://journal.info.unlp.edu.ar/wp-content/uploads/2015/papers_02/self.pdf
Aporte de:	SEDICI (UNLP) de Universidad Nacional de La Plata

id	I19-R120-10915-9385
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 genetic algorithms; self-adaptation; crossover; function optimisation Algoritmos evolutivos Optimización Informática
spellingShingle	Ciencias Informáticas genetic algorithms; self-adaptation; crossover; function optimisation Algoritmos evolutivos Optimización Informática Esquivel, Susana Cecilia Leiva, Héctor Ariel Gallard, Raúl Hector Self adaptation of parameters for MCPC in genetic algorithms
topic_facet	Ciencias Informáticas genetic algorithms; self-adaptation; crossover; function optimisation Algoritmos evolutivos Optimización Informática
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 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.
format	Articulo Articulo
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	2000
url	http://sedici.unlp.edu.ar/handle/10915/9385 http://journal.info.unlp.edu.ar/wp-content/uploads/2015/papers_02/self.pdf
work_keys_str_mv	AT esquivelsusanacecilia selfadaptationofparametersformcpcingeneticalgorithms AT leivahectorariel selfadaptationofparametersformcpcingeneticalgorithms AT gallardraulhector selfadaptationofparametersformcpcingeneticalgorithms
bdutipo_str	Repositorios
_version_	1764820491835539457

Self adaptation of parameters for MCPC in genetic algorithms

Ejemplares similares