A painless gradient-assisted multi-objective memetic mechanism for solving continuous bi-objective optimization problems

Adriana Lara López, Carlos A Coello Coello, Oliver Schütze

Research output: Contribution to conferencePaper

12 Citations (Scopus)

Abstract

In this work we present a simple way to introduce gradient-based information as a means to improve the search performed by a multi-objective evolutionary algorithm (MOEA). Our proposal can be easily incorporated into any MOEA, and is able to improve its performance when solving continuous bi-objective problems. We propose a novel mechanism to control the balance between the local search, and the global search performed by a MOEA. We discuss the advantages of the proposed method and its possible use when dealing with more objectives. Finally, we provide some guidelines regarding the use of our proposed approach. © 2010 IEEE.
Original languageAmerican English
DOIs
StatePublished - 1 Dec 2010
Externally publishedYes
Event2010 IEEE World Congress on Computational Intelligence, WCCI 2010 - 2010 IEEE Congress on Evolutionary Computation, CEC 2010 -
Duration: 1 Dec 2010 → …

Conference

Conference2010 IEEE World Congress on Computational Intelligence, WCCI 2010 - 2010 IEEE Congress on Evolutionary Computation, CEC 2010
Period1/12/10 → …

Fingerprint

Evolutionary algorithms
gradients
optimization
proposals

Cite this

López, A. L., Coello, C. A. C., & Schütze, O. (2010). A painless gradient-assisted multi-objective memetic mechanism for solving continuous bi-objective optimization problems. Paper presented at 2010 IEEE World Congress on Computational Intelligence, WCCI 2010 - 2010 IEEE Congress on Evolutionary Computation, CEC 2010, . https://doi.org/10.1109/CEC.2010.5586113
López, Adriana Lara ; Coello, Carlos A Coello ; Schütze, Oliver. / A painless gradient-assisted multi-objective memetic mechanism for solving continuous bi-objective optimization problems. Paper presented at 2010 IEEE World Congress on Computational Intelligence, WCCI 2010 - 2010 IEEE Congress on Evolutionary Computation, CEC 2010, .
@conference{339136fe2a0b4234b1595e36cd053d9f,
title = "A painless gradient-assisted multi-objective memetic mechanism for solving continuous bi-objective optimization problems",
abstract = "In this work we present a simple way to introduce gradient-based information as a means to improve the search performed by a multi-objective evolutionary algorithm (MOEA). Our proposal can be easily incorporated into any MOEA, and is able to improve its performance when solving continuous bi-objective problems. We propose a novel mechanism to control the balance between the local search, and the global search performed by a MOEA. We discuss the advantages of the proposed method and its possible use when dealing with more objectives. Finally, we provide some guidelines regarding the use of our proposed approach. {\circledC} 2010 IEEE.",
author = "L{\'o}pez, {Adriana Lara} and Coello, {Carlos A Coello} and Oliver Sch{\"u}tze",
year = "2010",
month = "12",
day = "1",
doi = "10.1109/CEC.2010.5586113",
language = "American English",
note = "2010 IEEE World Congress on Computational Intelligence, WCCI 2010 - 2010 IEEE Congress on Evolutionary Computation, CEC 2010 ; Conference date: 01-12-2010",

}

López, AL, Coello, CAC & Schütze, O 2010, 'A painless gradient-assisted multi-objective memetic mechanism for solving continuous bi-objective optimization problems', Paper presented at 2010 IEEE World Congress on Computational Intelligence, WCCI 2010 - 2010 IEEE Congress on Evolutionary Computation, CEC 2010, 1/12/10. https://doi.org/10.1109/CEC.2010.5586113

A painless gradient-assisted multi-objective memetic mechanism for solving continuous bi-objective optimization problems. / López, Adriana Lara; Coello, Carlos A Coello; Schütze, Oliver.

2010. Paper presented at 2010 IEEE World Congress on Computational Intelligence, WCCI 2010 - 2010 IEEE Congress on Evolutionary Computation, CEC 2010, .

Research output: Contribution to conferencePaper

TY - CONF

T1 - A painless gradient-assisted multi-objective memetic mechanism for solving continuous bi-objective optimization problems

AU - López, Adriana Lara

AU - Coello, Carlos A Coello

AU - Schütze, Oliver

PY - 2010/12/1

Y1 - 2010/12/1

N2 - In this work we present a simple way to introduce gradient-based information as a means to improve the search performed by a multi-objective evolutionary algorithm (MOEA). Our proposal can be easily incorporated into any MOEA, and is able to improve its performance when solving continuous bi-objective problems. We propose a novel mechanism to control the balance between the local search, and the global search performed by a MOEA. We discuss the advantages of the proposed method and its possible use when dealing with more objectives. Finally, we provide some guidelines regarding the use of our proposed approach. © 2010 IEEE.

AB - In this work we present a simple way to introduce gradient-based information as a means to improve the search performed by a multi-objective evolutionary algorithm (MOEA). Our proposal can be easily incorporated into any MOEA, and is able to improve its performance when solving continuous bi-objective problems. We propose a novel mechanism to control the balance between the local search, and the global search performed by a MOEA. We discuss the advantages of the proposed method and its possible use when dealing with more objectives. Finally, we provide some guidelines regarding the use of our proposed approach. © 2010 IEEE.

UR - https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=79959443072&origin=inward

UR - https://www.scopus.com/inward/citedby.uri?partnerID=HzOxMe3b&scp=79959443072&origin=inward

U2 - 10.1109/CEC.2010.5586113

DO - 10.1109/CEC.2010.5586113

M3 - Paper

ER -

López AL, Coello CAC, Schütze O. A painless gradient-assisted multi-objective memetic mechanism for solving continuous bi-objective optimization problems. 2010. Paper presented at 2010 IEEE World Congress on Computational Intelligence, WCCI 2010 - 2010 IEEE Congress on Evolutionary Computation, CEC 2010, . https://doi.org/10.1109/CEC.2010.5586113