Diversity-Driven Selection Operator for Combinatorial Optimization

Eduardo G. Carrano*, Felipe Campelo, Ricardo Takahashi

*Corresponding author for this work

Research output: Chapter in Book/Published conference outputConference publication


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 languageEnglish
Title of host publicationEvolutionary Multi-Criterion Optimization - 11th International Conference, EMO 2021, Proceedings
Subtitle of host publication EMO 2021: Evolutionary Multi-Criterion Optimization
EditorsHisao Ishibuchi, Qingfu Zhang, Ran Cheng, Ke Li, Hui Li, Handing Wang, Aimin Zhou
Number of pages13
ISBN (Electronic)978-3-030-72062-9
ISBN (Print)978-3-030-72061-2
Publication statusPublished - 24 Mar 2021
Event11th International Conference Series on Evolutionary Multi- Criterion Optimization - Shenzhen, China
Duration: 28 Mar 202131 Mar 2021
Conference number: 11

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume12654 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Conference11th International Conference Series on Evolutionary Multi- Criterion Optimization
Abbreviated titleEMO 2021

Bibliographical note

© 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


  • Combinatorial optimization
  • Diversity preservation
  • Genetic algorithms
  • Multiobjectivization
  • Selection operator


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