Studying the effect of techniques to generate reference vectors in many-objective optimization

Miriam Pescador-Rojas, Carlos A. Coello Coello

Producción científica: Capítulo del libro/informe/acta de congresoContribución a la conferenciarevisión exhaustiva

2 Citas (Scopus)

Resumen

A large number of Multi-Objective Evolutionary Algorithms employ reference directions in order to establish relative preferences for each objective function. Uniform Design (UD), Simplex Lattice Design (SLD) and their variants are techniques commonly used to generate a set of uniformly distributed reference directions with the aim of capturing the whole Pareto optimal front. In this paper, we present a comparative study of UD and SLD methods when solving Many-objective Optimization problems and we design a new strategy that combines SLD with multiple layers and UD techniques. Our preliminary results indicate that our proposed approach is able to outperform state-of-the-art methods in many-objective optimization problems.

Idioma originalInglés
Título de la publicación alojadaGECCO 2018 Companion - Proceedings of the 2018 Genetic and Evolutionary Computation Conference Companion
EditorialAssociation for Computing Machinery, Inc
Páginas193-194
Número de páginas2
ISBN (versión digital)9781450357647
DOI
EstadoPublicada - 6 jul. 2018
Publicado de forma externa
Evento2018 Genetic and Evolutionary Computation Conference, GECCO 2018 - Kyoto, Japón
Duración: 15 jul. 201819 jul. 2018

Serie de la publicación

NombreGECCO 2018 Companion - Proceedings of the 2018 Genetic and Evolutionary Computation Conference Companion

Conferencia

Conferencia2018 Genetic and Evolutionary Computation Conference, GECCO 2018
País/TerritorioJapón
CiudadKyoto
Período15/07/1819/07/18

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