Constructing Ensembles of Dispatching Rules for Multi-objective Problems
Created by W.Langdon from
gp-bibliography.bib Revision:1.8081
- @InProceedings{10.1007/978-3-031-06527-9_12,
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author = "Marko Durasevic and Lucija Planinic and
Francisco J. Gil-Gala and Domagoj Jakobovic",
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title = "Constructing Ensembles of Dispatching Rules for
Multi-objective Problems",
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booktitle = "Proceedings of the 9th International Work-Conference
on the Interplay Between Natural and Artificial
Computation, IWINAC 2022, Part II",
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year = "2022",
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editor = "Jose Manuel Ferrandez Vicente and
Jose Ramon Alvarez-Sanchez and Felix de la Paz Lopez and
Hojjat Adeli",
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volume = "13259",
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series = "LNCS",
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pages = "119--129",
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address = "Puerto de la Cruz, Tenerife, Spain",
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month = may # " 31-" # jun # " 3",
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publisher = "Springer",
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keywords = "genetic algorithms, genetic programming, Scheduling,
Unrelated machines, Dispatching rules, Ensembles,
Multi-objective optimisation",
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isbn13 = "978-3-031-06527-9",
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DOI = "doi:10.1007/978-3-031-06527-9_12",
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abstract = "Scheduling represents an important aspect of many
real-world processes, which is why such problems have
been well studied in the literature. Such problems are
often dynamic and require that multiple criteria be
optimised simultaneously. Dispatching rules (DRs) are
the method of choice for solving dynamic problems.
However, existing DRs are usually implemented for the
optimisation of only a single criterion. Since manual
design of DRs is difficult, genetic programming (GP)
has been used to automatically design new DRs for
single and multiple objectives. However, the
performance of a single rule is limited, and it may not
work well in all situations. Therefore, ensembles have
been used to create rule sets that outperform single
DRs. The goal of this study is to adapt ensemble
learning methods to create ensembles that optimise
multiple criteria simultaneously. The method creates
ensembles of DRs with multiple objectives previously
evolved by GP to improve their performance. The results
show that ensembles are suitable for the considered
multi-objective problem.",
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notes = "Published as Bio-inspired Systems and Applications:
from Robotics to Ambient Intelligence",
- }
Genetic Programming entries for
Marko Durasevic
Lucija Planinic
Francisco Javier Gil Gala
Domagoj Jakobovic
Citations