Combining single objective dispatching rules into multi-objective ensembles for the dynamic unrelated machines environment
Created by W.Langdon from
gp-bibliography.bib Revision:1.7970
- @Article{DURASEVIC:2023:swevo,
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author = "Marko Durasevic and Francisco Javier Gil-Gala and
Domagoj Jakobovic and Carlos A. {Coello Coello}",
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title = "Combining single objective dispatching rules into
multi-objective ensembles for the dynamic unrelated
machines environment",
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journal = "Swarm and Evolutionary Computation",
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volume = "80",
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pages = "101318",
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year = "2023",
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ISSN = "2210-6502",
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DOI = "doi:10.1016/j.swevo.2023.101318",
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URL = "https://www.sciencedirect.com/science/article/pii/S2210650223000913",
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keywords = "genetic algorithms, genetic programming, Dispatching
rules, Hyper-heuristic, Multi-objective optimisation,
Ensembles, Unrelated machines environment",
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abstract = "Dispatching rules (DRs), which are simple constructive
methods that incrementally build the schedule,
represent the most popular method for solving dynamic
scheduling problems. These DRs were usually designed
for optimising a single criterion and work poorly when
solving multi-objective (MO) problems. In recent years,
we have seen an increase of research dealing with
automated design of DRs using genetic programming (GP),
which has enabled the application of several
evolutionary MO optimisation methods to create DRs for
MO problems. However, for each considered MO problem
new DRs need to be evolved, which can be
computationally expensive. Motivated by this, we
propose a novel methodology to combine existing DRs
evolved for optimising individual criteria into
ensembles appropriate for optimising multiple criteria
simultaneously. For this purpose, we adapt the existing
simple ensemble construction (SEC) method to construct
ensembles of DRs for optimising MO problems. The method
is evaluated on several MO scheduling problems and
compared with DRs evolved by NSGA-II and NSGA-III. The
obtained results show that for most problems the
proposed method constructed ensembles that
significantly outperform DRs developed with standard MO
algorithms. Furthermore, we propose the application of
evolved MO rules and ensembles on problems with a
smaller number of criteria and demonstrate that with
such a strategy similar or better performance is
achieved compared to evolving DRs for such problems
directly, which demonstrates theif reusability and
generalisation potential",
- }
Genetic Programming entries for
Marko Durasevic
Francisco Javier Gil Gala
Domagoj Jakobovic
Carlos Artemio Coello Coello
Citations