Publicación:
Self-Adaptive Polynomial Mutation in Multi-Objective Evolutionary Algorithms

Vista sencilla de ítem
dc.contributor.authorCarles Bou, José Luis
dc.contributor.directorFernández Galán, Severino
dc.date.accessioned2024-05-20T12:38:26Z
dc.date.available2024-05-20T12:38:26Z
dc.date.issued2022-09-01
dc.description.abstractEvolutionary 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.en
dc.description.versionversión final
dc.identifier.urihttps://hdl.handle.net/20.500.14468/14657
dc.language.isoen
dc.publisherUniversidad Nacional de Educación a Distancia (España). Escuela Técnica Superior de Ingeniería Informática. Departamento de Inteligencia Artificial
dc.relation.centerFacultades y escuelas::E.T.S. de Ingeniería Informática
dc.relation.degreeMáster universitario en Investigación en Inteligencia Artificial
dc.relation.departmentInteligencia Artificial
dc.rightsinfo:eu-repo/semantics/openAccess
dc.rights.urihttps://creativecommons.org/licenses/by-nc-nd/4.0/deed.es
dc.subject.keywordsmulti-objective evolutionary algorithm
dc.subject.keywordsNSGA-II
dc.subject.keywordspolynomial mutation
dc.subject.keywordsdistribution index self-adaptation
dc.titleSelf-Adaptive Polynomial Mutation in Multi-Objective Evolutionary Algorithmses
dc.typetesis de maestríaes
dc.typemaster thesisen
dspace.entity.typePublication
Archivos
Bloque original
Mostrando 1 - 1 de 1
Miniatura
Nombre:
Carles_Bou_JoseLuis_TFM.pdf
Tamaño:
810.18 KB
Formato:
Adobe Portable Document Format
Descargar
Colecciones
Trabajos de fin de máster (TFM)