Constructing Ensembles of Automatically Designed Relocation Rules for the Container Relocation Problem
Created by W.Langdon from
gp-bibliography.bib Revision:1.7975
- @InProceedings{durasevic:2024:CEC,
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author = "Marko Durasevic and Mateja Dumic and
Francisco Javier Gil-Gala",
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title = "Constructing Ensembles of Automatically Designed
Relocation Rules for the Container Relocation Problem",
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booktitle = "2024 IEEE Congress on Evolutionary Computation (CEC)",
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year = "2024",
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editor = "Bing Xue",
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address = "Yokohama, Japan",
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month = "30 " # jun # " - 5 " # jul,
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publisher = "IEEE",
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keywords = "genetic algorithms, genetic programming, Evolutionary
computation, Containers, Ensemble learning, Marine
vehicles, Optimization, container relocation problem,
genetic program-ming",
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isbn13 = "979-8-3503-0837-2",
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DOI = "doi:10.1109/CEC60901.2024.10612112",
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abstract = "Automated design of heuristics with genetic
programming (G P) has, in recent years, become an
intensively researched research area. One of the most
recent applications of this methodology is to generate
relocation rules (RRs) for the container relocation
problem (CRP). CRP is an important combinatorial
optimisation problem that is found in ship ter-minals
and warehouses. RRs are simple constructive heuristic
methods that provide a good solution quickly, thus
representing an alternative to computationally
expensive exact or metaheuris-tic methods. Even though
the RRs designed by GP provide an improvement over
existing manually designed rules, they have limited
performance. An efficient way to improve the
performance of RRs generated by GP is to use ensemble
learning. In this study, we apply ensemble learning on
RRs generated by GP for CRP to improve the performance
of individual rules. We investigate how different
ensemble sizes and combination methods affect the
quality of the results, as well as which rules are
selected to form ensembles. The experimental study
shows that ensembles constructed out of automatically
designed RRs significantly improve performance compared
to the individual rules.",
-
notes = "also known as \cite{10612112}
WCCI 2024",
- }
Genetic Programming entries for
Marko Durasevic
Mateja Dumic
Francisco Javier Gil Gala
Citations