Does Size Matter? On the Influence of Ensemble Size on Constructing Ensembles of Dispatching Rules
Created by W.Langdon from
gp-bibliography.bib Revision:1.7970
- @InProceedings{durasevic:2023:GECCOcomp2,
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author = "Marko Durasevic and Francisco Javier Gil-Gala and
Domagoj Jakobovi\'{c}",
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title = "Does Size Matter? On the Influence of Ensemble Size on
Constructing Ensembles of Dispatching Rules",
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booktitle = "Proceedings of the 2023 Genetic and Evolutionary
Computation Conference",
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year = "2023",
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editor = "Sara Silva and Luis Paquete and Leonardo Vanneschi and
Nuno Lourenco and Ales Zamuda and Ahmed Kheiri and
Arnaud Liefooghe and Bing Xue and Ying Bi and
Nelishia Pillay and Irene Moser and Arthur Guijt and
Jessica Catarino and Pablo Garcia-Sanchez and
Leonardo Trujillo and Carla Silva and Nadarajen Veerapen",
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pages = "559--562",
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address = "Lisbon, Portugal",
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series = "GECCO '23",
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month = "15-19 " # jul,
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organisation = "SIGEVO",
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publisher = "Association for Computing Machinery",
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publisher_address = "New York, NY, USA",
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keywords = "genetic algorithms, genetic programming, dispatching
rules, unrelated machines environment, ensemble
construction: Poster",
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isbn13 = "9798400701191",
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DOI = "doi:10.1145/3583133.3590562",
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size = "4 pages",
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abstract = "Recent years saw an increase in the application of
genetic programming (GP) as a hyper-heuristic, i.e., a
method used to generate heuristics for solving various
combinatorial optimisation problems. One of its widest
application is in scheduling to automatically design
constructive heuristics called dispatching rules (DRs).
DRs are crucial for solving dynamic scheduling
environments, in which the conditions change over time.
Although automatically designed DRs achieve good
results, their performance is limited as a single DR
cannot always perform well. Therefore, various methods
were used to improve their performance, among which
ensemble learning represents one of the most promising
directions. Using ensembles introduces several new
parameters, such as the ensemble construction method,
ensemble collaboration method, and ensemble size. This
study investigates the possibility to remove the
ensemble size parameter when constructing ensembles.
Therefore, the simple ensemble combination method is
adapted to randomly select the size of the ensemble it
generates, rather than using a fixed ensemble size.
Experimental results demonstrate that not using a fixed
ensemble size does not result in a worse performance,
and that the best ensembles are of smaller sizes. This
shows that the ensemble size can be eliminated without
a significant influence on the performance.",
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notes = "GECCO-2023 A Recombination of the 32nd International
Conference on Genetic Algorithms (ICGA) and the 28th
Annual Genetic Programming Conference (GP)",
- }
Genetic Programming entries for
Marko Durasevic
Francisco Javier Gil Gala
Domagoj Jakobovic
Citations