Learning Penalisation Criterion of Guided Local Search for Large Scale Vehicle Routing Problem
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- @InProceedings{Cavalcanti-Costa:2021:SSCI,
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author = "Joao Guilherme {Cavalcanti Costa} and Yi Mei and
Mengjie Zhang",
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booktitle = "2021 IEEE Symposium Series on Computational
Intelligence (SSCI)",
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title = "Learning Penalisation Criterion of Guided Local Search
for Large Scale Vehicle Routing Problem",
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year = "2021",
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abstract = "A recent case of success, the Knowledge-Guided Local
Search was able to efficiently and effectively solve
several (Large-Scale) Vehicle Routing Problems. This
method presents an interesting concept of route
compactness in their search process and uses it to
penalise the solutions instead of using the traditional
distance measure. Although mostly being successful,
this measure sometimes leads to underperforming
solutions when compared to the distance aspect. Based
on the assumption that the best Guide Local Search
penalisation criterion depends on the VRP instance, we
make an analysis on how the algorithm behaves across
different instances and also propose a Machine Learning
model to learn to predict the best penalty criterion
for a given instance. Genetic Programming,
Support-Vector Machines and Random Forests are used in
this classification task. Additionally, we also
consider a regression model in order to estimate the
improvement given for each mode. Results show that it
is possible to find the correct class using the
selected features and, in fact, some models were able
to classify the majority of instances correctly.
However, this is not consistent across different
instances.",
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keywords = "genetic algorithms, genetic programming, Machine
learning algorithms, Computational modelling, Vehicle
routing, Predictive models, Search problems, Prediction
algorithms",
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DOI = "doi:10.1109/SSCI50451.2021.9659939",
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month = dec,
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notes = "Also known as \cite{9659939}",
- }
Genetic Programming entries for
Joao Guilherme Cavalcanti Costa
Yi Mei
Mengjie Zhang
Citations