Diversity-Driven Selection Operator for Combinatorial Optimization

Eduardo G. Carrano; Felipe Campelo; Ricardo Takahashi

doi:10.1007/978-3-030-72062-9_15

Diversity-Driven Selection Operator for Combinatorial Optimization

Eduardo G. Carrano^*, Felipe Campelo, Ricardo Takahashi

^*Corresponding author for this work

College of Engineering and Physical Sciences

Research output: Chapter in Book/Published conference output › Conference publication

Abstract

A new selection operator for genetic algorithms dedicated to combinatorial optimization, the Diversity Driven selection operator, is proposed. The proposed operator treats the population diversity as a second objective, in a multiobjectivization framework. The Diversity Driven operator is parameterless, and features low computational complexity. Numerical experiments were performed considering four different algorithms in 24 instances of seven combinatorial optimization problems, showing that it outperforms five classical selection schemes with regard to solution quality and convergence speed. Besides, the Diversity Driven selection operator delivers good and considerably different solutions in the final population, which can be useful as design alternatives.

Original language	English
Title of host publication	Evolutionary Multi-Criterion Optimization - 11th International Conference, EMO 2021, Proceedings
Subtitle of host publication	EMO 2021: Evolutionary Multi-Criterion Optimization
Editors	Hisao Ishibuchi, Qingfu Zhang, Ran Cheng, Ke Li, Hui Li, Handing Wang, Aimin Zhou
Publisher	Springer
Pages	178-190
Number of pages	13
ISBN (Electronic)	978-3-030-72062-9
ISBN (Print)	978-3-030-72061-2
DOIs	https://doi.org/10.1007/978-3-030-72062-9_15
Publication status	Published - 24 Mar 2021
Event	11th International Conference Series on Evolutionary Multi- Criterion Optimization - Shenzhen, China Duration: 28 Mar 2021 → 31 Mar 2021 Conference number: 11

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	12654 LNCS
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	11th International Conference Series on Evolutionary Multi- Criterion Optimization
Abbreviated title	EMO 2021
Country/Territory	China
City	Shenzhen
Period	28/03/21 → 31/03/21

Bibliographical note

Keywords

Combinatorial optimization
Diversity preservation
Genetic algorithms
Multiobjectivization
Selection operator

Access to Document

10.1007/978-3-030-72062-9_15

Diversity-Driven Selection Operator for Combinatorial Optimization
© Springer Nature B.V. 2021. The final publication is available at Springer via http://dx.doi.org/10.1007/978-3-030-72062-9_15
Accepted author manuscript, 295 KB

Cite this

Carrano, E. G., Campelo, F., & Takahashi, R. (2021). Diversity-Driven Selection Operator for Combinatorial Optimization. In H. Ishibuchi, Q. Zhang, R. Cheng, K. Li, H. Li, H. Wang, & A. Zhou (Eds.), Evolutionary Multi-Criterion Optimization - 11th International Conference, EMO 2021, Proceedings: EMO 2021: Evolutionary Multi-Criterion Optimization (pp. 178-190). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 12654 LNCS). Springer. https://doi.org/10.1007/978-3-030-72062-9_15

Carrano, Eduardo G. ; Campelo, Felipe ; Takahashi, Ricardo. / Diversity-Driven Selection Operator for Combinatorial Optimization. Evolutionary Multi-Criterion Optimization - 11th International Conference, EMO 2021, Proceedings: EMO 2021: Evolutionary Multi-Criterion Optimization. editor / Hisao Ishibuchi ; Qingfu Zhang ; Ran Cheng ; Ke Li ; Hui Li ; Handing Wang ; Aimin Zhou. Springer, 2021. pp. 178-190 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

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abstract = "A new selection operator for genetic algorithms dedicated to combinatorial optimization, the Diversity Driven selection operator, is proposed. The proposed operator treats the population diversity as a second objective, in a multiobjectivization framework. The Diversity Driven operator is parameterless, and features low computational complexity. Numerical experiments were performed considering four different algorithms in 24 instances of seven combinatorial optimization problems, showing that it outperforms five classical selection schemes with regard to solution quality and convergence speed. Besides, the Diversity Driven selection operator delivers good and considerably different solutions in the final population, which can be useful as design alternatives.",

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Carrano, EG, Campelo, F & Takahashi, R 2021, Diversity-Driven Selection Operator for Combinatorial Optimization. in H Ishibuchi, Q Zhang, R Cheng, K Li, H Li, H Wang & A Zhou (eds), Evolutionary Multi-Criterion Optimization - 11th International Conference, EMO 2021, Proceedings: EMO 2021: Evolutionary Multi-Criterion Optimization. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 12654 LNCS, Springer, pp. 178-190, 11th International Conference Series on Evolutionary Multi- Criterion Optimization, Shenzhen, China, 28/03/21. https://doi.org/10.1007/978-3-030-72062-9_15

Diversity-Driven Selection Operator for Combinatorial Optimization. / Carrano, Eduardo G.; Campelo, Felipe; Takahashi, Ricardo.
Evolutionary Multi-Criterion Optimization - 11th International Conference, EMO 2021, Proceedings: EMO 2021: Evolutionary Multi-Criterion Optimization. ed. / Hisao Ishibuchi; Qingfu Zhang; Ran Cheng; Ke Li; Hui Li; Handing Wang; Aimin Zhou. Springer, 2021. p. 178-190 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 12654 LNCS).

Research output: Chapter in Book/Published conference output › Conference publication

TY - GEN

T1 - Diversity-Driven Selection Operator for Combinatorial Optimization

AU - Carrano, Eduardo G.

AU - Campelo, Felipe

AU - Takahashi, Ricardo

N1 - Conference code: 11

PY - 2021/3/24

Y1 - 2021/3/24

N2 - A new selection operator for genetic algorithms dedicated to combinatorial optimization, the Diversity Driven selection operator, is proposed. The proposed operator treats the population diversity as a second objective, in a multiobjectivization framework. The Diversity Driven operator is parameterless, and features low computational complexity. Numerical experiments were performed considering four different algorithms in 24 instances of seven combinatorial optimization problems, showing that it outperforms five classical selection schemes with regard to solution quality and convergence speed. Besides, the Diversity Driven selection operator delivers good and considerably different solutions in the final population, which can be useful as design alternatives.

AB - A new selection operator for genetic algorithms dedicated to combinatorial optimization, the Diversity Driven selection operator, is proposed. The proposed operator treats the population diversity as a second objective, in a multiobjectivization framework. The Diversity Driven operator is parameterless, and features low computational complexity. Numerical experiments were performed considering four different algorithms in 24 instances of seven combinatorial optimization problems, showing that it outperforms five classical selection schemes with regard to solution quality and convergence speed. Besides, the Diversity Driven selection operator delivers good and considerably different solutions in the final population, which can be useful as design alternatives.

KW - Combinatorial optimization

KW - Diversity preservation

KW - Genetic algorithms

KW - Multiobjectivization

KW - Selection operator

UR - https://link.springer.com/chapter/10.1007%2F978-3-030-72062-9_15

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DO - 10.1007/978-3-030-72062-9_15

M3 - Conference publication

SN - 978-3-030-72061-2

T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

SP - 178

EP - 190

BT - Evolutionary Multi-Criterion Optimization - 11th International Conference, EMO 2021, Proceedings

A2 - Ishibuchi, Hisao

A2 - Zhang, Qingfu

A2 - Cheng, Ran

A2 - Li, Ke

A2 - Li, Hui

A2 - Wang, Handing

A2 - Zhou, Aimin

PB - Springer

T2 - 11th International Conference Series on Evolutionary Multi- Criterion Optimization

Y2 - 28 March 2021 through 31 March 2021

ER -

Carrano EG, Campelo F, Takahashi R. Diversity-Driven Selection Operator for Combinatorial Optimization. In Ishibuchi H, Zhang Q, Cheng R, Li K, Li H, Wang H, Zhou A, editors, Evolutionary Multi-Criterion Optimization - 11th International Conference, EMO 2021, Proceedings: EMO 2021: Evolutionary Multi-Criterion Optimization. Springer. 2021. p. 178-190. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-030-72062-9_15

Diversity-Driven Selection Operator for Combinatorial Optimization

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Keywords

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