Multitasking Genetic Programming for Stochastic Team Orienteering Problem with Time Windows
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- @InProceedings{Karunakaran:2019:SSCI,
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author = "Deepak Karunakaran and Yi Mei and Mengjie Zhang",
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booktitle = "2019 IEEE Symposium Series on Computational
Intelligence (SSCI)",
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title = "Multitasking Genetic Programming for Stochastic Team
Orienteering Problem with Time Windows",
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year = "2019",
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pages = "1598--1605",
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month = dec,
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keywords = "genetic algorithms, genetic programming",
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DOI = "doi:10.1109/SSCI44817.2019.9002804",
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abstract = "The tourism industry is witnessing high growth in
recent years leading to a large number of options for a
tourist. Personalised tourist trip design is faced with
many places of interests, different tourist preferences
and uncertainty in visit duration. In this paper, we
study the stochastic Team Orienteering Problem with
Time Windows (TOPTW) that well models the personalised
tourist trip design. Under an uncertain environment,
determining a robust solution in advance is not very
effective due to frequent changes in the trip. Reactive
decision-making policies have shown to be effective
alternatives. Genetic programming-based hyper-heuristic
(GPHH) approaches have been explored to automatically
design policies. However, GPHH is computationally
intensive. Considering a large number of trip design
scenarios (e.g. cities), evolving a policy for each of
these scenarios individually is difficult and time
consuming. In this work, we propose a multitasking GPHH
approach based on island model to evolve a set of
policies which are effective across multiple trip
design scenarios. The experimental studies show that
our multitasking approach which needs only a single run
to evolve policies for all problem instances is both
efficient and effective when compared with the standard
GPHH approach which requires a separate population for
each TOPTW instance and runs sequentially.",
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notes = "Also known as \cite{9002804}",
- }
Genetic Programming entries for
Deepak Karunakaran
Yi Mei
Mengjie Zhang
Citations