An efficient genetic programming approach to design priority rules for resource-constrained project scheduling problem
Created by W.Langdon from
gp-bibliography.bib Revision:1.8097
- @Article{Luo:2022:ESA,
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author = "Jingyu Luo and Mario Vanhoucke and Jose Coelho and
Weikang Guo",
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title = "An efficient genetic programming approach to design
priority rules for resource-constrained project
scheduling problem",
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journal = "Expert Systems with Applications",
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year = "2022",
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volume = "198",
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pages = "116753",
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month = "15 " # jul,
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keywords = "genetic algorithms, genetic programming,
Resource-constrained project scheduling, Priority
rules",
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ISSN = "0957-4174",
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URL = "https://www.sciencedirect.com/science/article/pii/S0957417422002196",
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DOI = "doi:10.1016/j.eswa.2022.116753",
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abstract = "In recent years, machine learning techniques,
especially genetic programming (GP), have been a
powerful approach for automated design of the priority
rule-heuristics for the resource-constrained project
scheduling problem (RCPSP). However, it requires
intensive computing effort, carefully selected training
data and appropriate assessment criteria. This research
proposes a GP hyper-heuristic method with a duplicate
removal technique to create new priority rules that
outperform the traditional rules. The experiments have
verified the efficiency of the proposed algorithm as
compared to the standard GP approach. Furthermore, the
impact of the training data selection and fitness
evaluation have also been investigated. The results
show that a compact training set can provide good
output and existing evaluation methods are all usable
for evolving efficient priority rules. The priority
rules designed by the proposed approach are tested on
extensive existing datasets and newly generated large
projects with more than 1000 activities. In order to
achieve better performance on small-sized projects, we
also develop a method to combine rules as efficient
ensembles. Computational comparisons between
GP-designed rules and traditional priority rules
indicate the superiority and generalization capability
of the proposed GP algorithm in solving the RCPSP.",
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notes = "Also known as \cite{LUO2022116753}
\cite{DBLP:journals/eswa/LuoVCG22}",
- }
Genetic Programming entries for
Jingyu Luo
Mario Vanhoucke
Jose Coelho
Weikang Guo
Citations