Self-Adaptive Polynomial Mutation in Multi-Objective Evolutionary Algorithms

Carles Bou, José Luis. (2022). Self-Adaptive Polynomial Mutation in Multi-Objective Evolutionary Algorithms Master Thesis, Universidad Nacional de Educación a Distancia (España). Escuela Técnica Superior de Ingeniería Informática. Departamento de Inteligencia Artificial

Ficheros (Some files may be inaccessible until you login with your e-spacio credentials)
Nombre Descripción Tipo MIME Size
Carles_Bou_JoseLuis_TFM.pdf Carles_Bou_JoseLuis_TFM.pdf application/pdf 810.17KB

Título Self-Adaptive Polynomial Mutation in Multi-Objective Evolutionary Algorithms
Autor(es) Carles Bou, José Luis
Abstract Evolutionary multi-objective optimization is a field that has experienced a rapid growth in the last two decades. Although an important number of new multi-objective evolutionary algorithms have been designed by the scientific community, the popular Non-Dominated Sorting Genetic Algorithm (NSGAII) remains as a widely used baseline for performance comparison purposes. Since every evolutionary algorithm needs several parameters to be set up in order to operate, parameter control constitutes a crucial task for the effective and efficient performance of multi-objective evolutionary algorithms. However, despite the advancements in parameter control for evolutionary algorithms, NSGA-II has been mainly used in the literature with fine-tuned static parameters. This paper introduces a novel and computationally lightweight self-adaptation mechanism for controlling the distribution index parameter of the polynomial mutation operator usually employed by NSGA-II in particular and by multi-objective evolutionary algorithms in general. Additionally, the classical NSGA-II using polynomial mutation with a static distribution index is compared with this new version utilizing a self-adapted parameter. The experiments carried out over twenty-five benchmark problems using three quality indicators (hypervolume, generalized spread, and modified inverted generational distance) show that the proposed self-adaptive mutator variant outperforms its static counterpart in most of the cases. This result supports the potential of self-adaptive parameter control in multi-objective evolutionary algorithms.
Notas adicionales Trabajo de Fin de Máster Universitario en Investigación en Inteligencia Artificial. UNED
Materia(s) Ingeniería Informática
Palabra clave multi-objective evolutionary algorithm
NSGA-II
polynomial mutation
distribution index self-adaptation
Editor(es) Universidad Nacional de Educación a Distancia (España). Escuela Técnica Superior de Ingeniería Informática. Departamento de Inteligencia Artificial
Director/Tutor Fernández Galán, Severino
Fecha 2022-09-01
Formato application/pdf
Identificador bibliuned:master-ETSInformatica-IIA-Jlcarles
http://e-spacio.uned.es/fez/view/bibliuned:master-ETSInformatica-IIA-Jlcarles
Idioma eng
Versión de la publicación acceptedVersion
Nivel de acceso y licencia http://creativecommons.org/licenses/by-nc-nd/4.0
info:eu-repo/semantics/openAccess
Tipo de recurso master Thesis
Tipo de acceso Acceso abierto

 
Versiones
Versión Tipo de filtro
Contador de citas: Google Scholar Search Google Scholar
Estadísticas de acceso: 99 Visitas, 49 Descargas  -  Estadísticas en detalle
Creado: Tue, 12 Sep 2023, 20:06:40 CET