Genetic Programming Hyper-heuristic for Stochastic Team Orienteering Problem with Time Windows
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gp-bibliography.bib Revision:1.8098
- @InProceedings{Mei:2018:CEC,
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author = "Yi Mei and Mengjie Zhang",
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title = "Genetic Programming Hyper-heuristic for Stochastic
Team Orienteering Problem with Time Windows",
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booktitle = "2018 IEEE Congress on Evolutionary Computation (CEC)",
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year = "2018",
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editor = "Marley Vellasco",
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address = "Rio de Janeiro, Brazil",
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month = "8-13 " # jul,
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publisher = "IEEE",
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keywords = "genetic algorithms, genetic programming",
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DOI = "doi:10.1109/CEC.2018.8477983",
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abstract = "This paper investigates the stochastic team
orienteering problem with time windows, which is a well
known problem to model personalised tourist trip
design. Specifically, we consider the stochastic visit
duration, which may make preplanned trip infeasible.
Existing studies focus on optimising robust solutions
in advance, which is not effective in adjusting the
subsequent trip in real time. Decision making policies,
on the other hand, are effective heuristics to this
end. However, it is very challenging to manually design
effective policies. In this paper, we investigate
automatically evolving policies for the stochastic team
orienteering problem with time windows by genetic
programming hyper-heuristics. We designed novel
problem-specific features for the terminal set, and a
meta-algorithm for fitness evaluation. Furthermore, we
developed two look-ahead features that can provide more
fruitful information than the basic features for
real-time decision making. The experimental studies
showed that the proposed genetic programming
hyper-heuristic can evolve policies that are much
better than the manually designed policies. In
addition, it seems that the look-ahead features are not
so effective when directly included in the terminals.
This suggests the requirement of more intelligent ways
of incorporating lookahead information.",
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notes = "WCCI2018",
- }
Genetic Programming entries for
Yi Mei
Mengjie Zhang
Citations