Genetic Programming Hyper-heuristic with Cluster Awareness for Stochastic Team Orienteering Problem with Time Windows
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- @InProceedings{Jackson:2020:CEC,
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author = "Jericho Jackson and Yi Mei",
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booktitle = "2020 IEEE Congress on Evolutionary Computation (CEC)",
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title = "Genetic Programming Hyper-heuristic with Cluster
Awareness for Stochastic Team Orienteering Problem with
Time Windows",
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year = "2020",
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editor = "Yaochu Jin",
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month = "19-24 " # jul,
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keywords = "genetic algorithms, genetic programming, Clustering
algorithms, Schedules, Stochastic processes, Real-time
systems, Heuristic algorithms, Decision making",
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isbn13 = "978-1-7281-6929-3",
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DOI = "doi:10.1109/CEC48606.2020.9185911",
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abstract = "This paper looks at the stochastic Team orienteering
Problem with Time Windows, a well-known problem that
models the Personalised Tourist Trip Design Problem.
Due to the nature of randomness such as real-time
delays, the traditional optimisation approaches are not
effective in solving the stochastic problem variant. In
this case, genetic programming hyper-heuristics (GPHH)
are promising techniques for automatically learning
heuristics to make real-time decisions to effectively
handle the stochastic environment, however, they still
have limitations as the decision making policies use
short-sighted information. In this paper, we propose to
incorporate global information into the GPHH solution,
with a constructed terminal feature based on cluster
information to be used by the GPHH, as well as a
clustering-aware solution generation process. The
experimental studies showed that the newly designed
cluster-based feature gave an improvement over the
standard GPHH solution. This suggests that
incorporating cluster information can be beneficial.
Although the clustering-aware solution generation
process did not achieve satisfactory performance, the
further analysis showed that it could lead to improved
performance under certain condition. Overall we
demonstrate the effectiveness of using clustering as a
global information to enhance the performance of
GPHH.",
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notes = "Also known as \cite{9185911}",
- }
Genetic Programming entries for
Jericho Jackson
Yi Mei
Citations