Heuristic Ensemble Construction Methods of Automatically Designed Dispatching Rules for the Unrelated Machines Environment
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- @Article{durasevic:2024:Axioms,
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author = "Marko Durasevic and Domagoj Jakobovic",
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title = "Heuristic Ensemble Construction Methods of
Automatically Designed Dispatching Rules for the
Unrelated Machines Environment",
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journal = "Axioms",
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year = "2024",
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volume = "13",
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number = "1",
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pages = "Article No. 37",
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keywords = "genetic algorithms, genetic programming, scheduling,
unrelated machines environment, ensemble learning,
dispatching rules",
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ISSN = "2075-1680",
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URL = "https://www.mdpi.com/2075-1680/13/1/37",
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DOI = "doi:10.3390/axioms13010037",
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abstract = "Dynamic scheduling represents an important class of
combinatorial optimisation problems that are usually
solved with simple heuristics, the so-called
dispatching rules (DRs). Designing efficient DRs is a
tedious task, which is why it has been automated
through the application of genetic programming (GP).
Various approaches have been used to improve the
results of automatically generated DRs, with ensemble
learning being one of the best-known. The goal of
ensemble learning is to create sets of automatically
designed DRs that perform better together. One of the
main problems in ensemble learning is the selection of
DRs to form the ensemble. To this end, various ensemble
construction methods have been proposed over the years.
However, these methods are quite computationally
intensive and require a lot of computation time to
obtain good ensembles. Therefore, in this study, we
propose several simple heuristic ensemble construction
methods that can be used to construct ensembles quite
efficiently and without the need to evaluate their
performance. The proposed methods construct the
ensembles solely based on certain properties of the
individual DRs used for their construction. The
experimental study shows that some of the proposed
heuristic construction methods perform better than more
complex state-of-the-art approaches for constructing
ensembles.",
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notes = "also known as \cite{axioms13010037}",
- }
Genetic Programming entries for
Marko Durasevic
Domagoj Jakobovic
Citations