Diversity-Driven Selection Operator for Combinatorial Optimization

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.

Publication DOI: https://doi.org/10.1007/978-3-030-72062-9_15
Divisions: College of Engineering & Physical Sciences > School of Informatics and Digital Engineering > Computer Science
College of Engineering & Physical Sciences
Additional Information: © 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
Event Title: 11th International Conference Series on Evolutionary Multi- Criterion Optimization
Event Type: Other
Event Dates: 2021-03-28 - 2021-03-31
Uncontrolled Keywords: Combinatorial optimization,Diversity preservation,Genetic algorithms,Multiobjectivization,Selection operator,Theoretical Computer Science,Computer Science(all)
ISBN: 978-3-030-72061-2, 978-3-030-72062-9
Full Text Link:
Related URLs: https://link.sp ... -030-72062-9_15 (Publisher URL)
http://www.scop ... tnerID=8YFLogxK (Scopus URL)
PURE Output Type: Conference contribution
Published Date: 2021-03-24
Accepted Date: 2021-03-01
Authors: Carrano, Eduardo G.
Campelo, Felipe (ORCID Profile 0000-0001-8432-4325)
Takahashi, Ricardo

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Version: Accepted Version

Access Restriction: Restricted to Repository staff only until 24 March 2022.


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