Assessing the Ability of Genetic Programming for Feature Selection in Constructing Dispatching Rules for Unrelated Machine Environments
Created by W.Langdon from
gp-bibliography.bib Revision:1.8098
- @Article{durasevic:2024:Algorithms,
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author = "Marko Durasevic and Domagoj Jakobovic and
Stjepan Picek and Luca Mariot",
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title = "Assessing the Ability of Genetic Programming for
Feature Selection in Constructing Dispatching Rules for
Unrelated Machine Environments",
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journal = "Algorithms",
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year = "2024",
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volume = "17",
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number = "2",
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pages = "Article No. 67",
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note = "Special Issue Algorithms for Feature Selection (2nd
Edition)",
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keywords = "genetic algorithms, genetic programming, feature
selection, unrelated machine problem, feature
selection, scheduling",
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ISSN = "1999-4893",
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URL = "https://www.mdpi.com/1999-4893/17/2/67",
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DOI = "doi:10.3390/a17020067",
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abstract = "The automated design of dispatching rules (DRs) with
genetic programming (GP) has become an important
research direction in recent years. One of the most
important decisions in applying GP to generate DRs is
determining the features of the scheduling problem to
be used during the evolution process. Unfortunately,
there are no clear rules or guidelines for the design
or selection of such features, and often the features
are simply defined without investigating their
influence on the performance of the algorithm. However,
the performance of GP can depend significantly on the
features provided to it, and a poor or inadequate
selection of features for a given problem can result in
the algorithm performing poorly. In this study, we
examine in detail the features that GP should use when
developing DRs for unrelated machine scheduling
problems. Different types of features are investigated,
and the best combination of these features is
determined using two selection methods. The obtained
results show that the design and selection of
appropriate features are crucial for GP, as they
improve the results by about 7percent when only the
simplest terminal nodes are used without selection. In
addition, the results show that it is not possible to
outperform more sophisticated manually designed DRs
when only the simplest problem features are used as
terminal nodes. This shows how important it is to
design appropriate composite terminal nodes to produce
high-quality DRs.",
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notes = "also known as \cite{a17020067}",
- }
Genetic Programming entries for
Marko Durasevic
Domagoj Jakobovic
Stjepan Picek
Luca Mariot
Citations