To bias or not to bias: Probabilistic initialisation for evolving dispatching rules
Created by W.Langdon from
gp-bibliography.bib Revision:1.7970
- @InProceedings{Durasevic:2023:EuroGP,
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author = "Marko Durasevic and Francisco Javier Gil-Gala and
Domagoj Jakobovic",
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title = "To bias or not to bias: Probabilistic initialisation
for evolving dispatching rules",
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booktitle = "EuroGP 2023: Proceedings of the 26th European
Conference on Genetic Programming",
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year = "2023",
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month = "12-14 " # apr,
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editor = "Gisele Pappa and Mario Giacobini and Zdenek Vasicek",
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series = "LNCS",
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volume = "13986",
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publisher = "Springer Verlag",
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address = "Brno, Czech Republic",
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pages = "308--323",
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organisation = "EvoStar, Species",
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keywords = "genetic algorithms, genetic programming, Dispatching
rules, Unrelated machines environment, Scheduling,
Individual initialisation: Poster",
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isbn13 = "978-3-031-29572-0",
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URL = "https://rdcu.be/c8U3i",
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DOI = "doi:10.1007/978-3-031-29573-7_20",
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size = "16 pages",
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abstract = "he automatic generation of dispatching rules (DRs) for
various scheduling problems using genetic programming
(GP) has become an increasingly researched topic in
recent years. Creating DRs in this way relieves domain
experts of the tedious task of manually designing new
rules, but also often leads to the discovery of better
rules than those already available. However, developing
new DRs is a computationally intensive process that
takes time to converge to good solutions. One possible
way to improve the convergence of evolutionary
algorithms is to use a more sophisticated method to
generate the initial population of individuals. In this
paper, we propose a simple method for initialising
individuals that uses probabilistic information from
previously evolved DRs. The method extracts the
information on how many times each node occurs at each
level of the tree and in each context. This information
is then used to introduce bias in the selection of the
node to be selected at a particular position during the
construction of the expression tree. The experiments
show that with the proposed method it is possible to
improve the convergence of GP when generating new DRs,
so that GP can obtain high-quality DRs in a much
shorter time.",
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notes = "Part of \cite{Pappa:2023:GP} EuroGP'2023 held in
conjunction with EvoCOP2023, EvoMusArt2023 and
EvoApplications2023",
- }
Genetic Programming entries for
Marko Durasevic
Francisco Javier Gil Gala
Domagoj Jakobovic
Citations