A preference-based multi-objective evolutionary algorithm for approximating a region of interest

Filatovas, Ernestas; Kurasova, Olga; Redondo, Juana López; Fernández, José

Use this URL to cite this publication: https://hdl.handle.net/20.500.14911/116225

A preference-based multi-objective evolutionary algorithm for approximating a region of interest

Details

Publication Type (CRIS)

Konferencijų tezės nerecenzuojamame leidinyje / Conference theses in non-peer-reviewed publication (T2)

Publication Type (eLABa)

Konferencijos pranešimo tezės / Conference presentation abstract (T2)

VILNIUS TECH Research Priorities and Topics

Fizinių, technologinių ir ekonominių procesų matematiniai modeliai ir metodai / Mathematical models and methods of physical, technological and economic processes (FM03)

Lithuanian Intelligent Specialization

Transportas, logistika ir informacinės ir ryšių technologijos (IRT) / Transport, logistic and information and communication technologies (L106)

Author(s)

Author	Affiliation
Filatovas, Ernestas	Grafinių sistemų katedra / Department of Graphical Systems (1048)
Kurasova, Olga	Vilniaus universitetas
Redondo, Juana López	University of Almería
Fernández, José	University of Murcia

Title

A preference-based multi-objective evolutionary algorithm for approximating a region of interest

[en]

Ernestas Filatovas, Olga Kurasova, Juana López Redondo, José Fernández

Is part of

GOW’16 : proceedings of the XIII Global Optimization Workshop, 4-8 September 2016 : [extended abstracts]

Published In

Year	Start Page	End Page
2016	21	24

Publisher

Braga, Portugal : University of Minho, 2016

Extent

p. 21-24

URI

URI
https://hdl.handle.net/20.500.14911/116225

Cited references

Science / Art Area

Technologijos mokslai / Technological sciences (T)

Field of Science / Art

Informatikos inžinerija / Informatics engineering (T007)

Keywords (en)

Abstract (en)

Multi-objective preference-based evolutionary algorithms approximate the part of the Pareto front that meets the preference information expressed by the Decision Maker. However, only a few of such algorithms are able to obtain well-distributed solutions covering the complete “region of interest”. In this work a preference-based evolutionary algorithm for approximating the region of interest of multi-objective optimization problems is proposed. The efficiency of the proposed algorithm has been experimentally evaluated and compared to another state-of-the-art multi-objective preference- based evolutionary algorithm by solving a set of multi-objective optimization benchmark problems.