On gradient-based local search to hybridize multi-objective evolutionary algorithms

Adriana Lara; Oliver Schütze; Carlos A. Coello Coello

doi:10.1007/978-3-642-32726-1_9

On gradient-based local search to hybridize multi-objective evolutionary algorithms

Adriana Lara, Oliver Schütze, Carlos A. Coello Coello

Escuela Superior de Física y Matemáticas (ESFM)

Research output: Chapter in Book/Report/Conference proceeding › Chapter › peer-review

6 Scopus citations

Abstract

Using evolutionary algorithms when solving multi-objective optimization problems (MOPs) has shown remarkable results during the last decade. As a consolidated research area it counts with a number of guidelines and processes; even though, their efficiency is still a big issue which lets room for improvements. In this chapter we explore the use of gradient-based information to increase efficiency on evolutionary methods, when dealing with smooth real-valued MOPs. We show the main aspects to be considered when building local search operators using the objective function gradients, and when coupling them with evolutionary algorithms. We present an overview of our current methods with discussion about their convenience for particular kinds of problems.

Translated title of the contribution	En la búsqueda local basada en gradientes para hibridar algoritmos evolutivos multiobjetivo
Original language	English
Title of host publication	EVOLVE- A Bridge between Probability, Set Oriented Numerics and Evolutionary Computation
Editors	Emilia Tantar, Pascal Bouvry, Alexandru-Adrian Tantar, Pierre Del Moral, Pierrick Legrand, Pierre Del Moral, Pierrick Legrand, Carlos Coello Coello, Oliver Schutze
Pages	305-332
Number of pages	28
DOIs	https://doi.org/10.1007/978-3-642-32726-1_9
State	Published - 2013

Publication series

Name	Studies in Computational Intelligence
Volume	447
ISSN (Print)	1860-949X

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10.1007/978-3-642-32726-1_9

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Lara, A., Schütze, O., & Coello Coello, C. A. (2013). On gradient-based local search to hybridize multi-objective evolutionary algorithms. In E. Tantar, P. Bouvry, A.-A. Tantar, P. Del Moral, P. Legrand, P. Del Moral, P. Legrand, C. Coello Coello, & O. Schutze (Eds.), EVOLVE- A Bridge between Probability, Set Oriented Numerics and Evolutionary Computation (pp. 305-332). (Studies in Computational Intelligence; Vol. 447). https://doi.org/10.1007/978-3-642-32726-1_9

Lara, Adriana ; Schütze, Oliver ; Coello Coello, Carlos A. / On gradient-based local search to hybridize multi-objective evolutionary algorithms. EVOLVE- A Bridge between Probability, Set Oriented Numerics and Evolutionary Computation. editor / Emilia Tantar ; Pascal Bouvry ; Alexandru-Adrian Tantar ; Pierre Del Moral ; Pierrick Legrand ; Pierre Del Moral ; Pierrick Legrand ; Carlos Coello Coello ; Oliver Schutze. 2013. pp. 305-332 (Studies in Computational Intelligence).

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Lara, A, Schütze, O & Coello Coello, CA 2013, On gradient-based local search to hybridize multi-objective evolutionary algorithms. in E Tantar, P Bouvry, A-A Tantar, P Del Moral, P Legrand, P Del Moral, P Legrand, C Coello Coello & O Schutze (eds), EVOLVE- A Bridge between Probability, Set Oriented Numerics and Evolutionary Computation. Studies in Computational Intelligence, vol. 447, pp. 305-332. https://doi.org/10.1007/978-3-642-32726-1_9

On gradient-based local search to hybridize multi-objective evolutionary algorithms. / Lara, Adriana; Schütze, Oliver; Coello Coello, Carlos A.
EVOLVE- A Bridge between Probability, Set Oriented Numerics and Evolutionary Computation. ed. / Emilia Tantar; Pascal Bouvry; Alexandru-Adrian Tantar; Pierre Del Moral; Pierrick Legrand; Pierre Del Moral; Pierrick Legrand; Carlos Coello Coello; Oliver Schutze. 2013. p. 305-332 (Studies in Computational Intelligence; Vol. 447).

Research output: Chapter in Book/Report/Conference proceeding › Chapter › peer-review

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Lara A, Schütze O, Coello Coello CA. On gradient-based local search to hybridize multi-objective evolutionary algorithms. In Tantar E, Bouvry P, Tantar AA, Del Moral P, Legrand P, Del Moral P, Legrand P, Coello Coello C, Schutze O, editors, EVOLVE- A Bridge between Probability, Set Oriented Numerics and Evolutionary Computation. 2013. p. 305-332. (Studies in Computational Intelligence). doi: 10.1007/978-3-642-32726-1_9

On gradient-based local search to hybridize multi-objective evolutionary algorithms

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