Look-Ahead Genetic Programming for Uncertain Capacitated Arc Routing Problem
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- @InProceedings{MacLachlan:2021:CEC,
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author = "Jordan MacLachlan and Yi Mei",
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booktitle = "2021 IEEE Congress on Evolutionary Computation (CEC)",
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title = "Look-Ahead Genetic Programming for Uncertain
Capacitated Arc Routing Problem",
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year = "2021",
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editor = "Yew-Soon Ong",
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pages = "1872--1879",
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address = "Krakow, Poland",
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month = "28 " # jun # "-1 " # jul,
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isbn13 = "978-1-7281-8393-0",
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abstract = "Genetic Programming Hyper-Heuristic (GPHH) has been
successfully applied to evolve routing policies for the
Uncertain Capacitated Arc Routing Problem (UCARP).
However, the current GPHH approaches have a limitation
that they only consider myopic information of the
current decision step. In this paper, we proposed
incorporating look-ahead information to the decision
process of GP-evolved routing policies. We designed a
number of potentially promising chains of candidate
tasks, and expand the candidate task pool to consider
both the single tasks and task chains. This way, the
routing policy can consider the look-ahead information
incorporated in the considered task chains. The
proposed GP with Chain Policies (GPCP) was compared
with the standard GPHH on a range of UCARP instances,
and the results showed that the task chains can improve
the effectiveness of the routing policies sometimes.
The better performance of a routing policy largely
depends on whether it can balance the selections of
single tasks and task chains, and whether it can stick
to the whole selected chain rather than only the first
task of the chain. In addition, there are some abnormal
runs with serious overfitting issue that we will
address in our future work.",
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keywords = "genetic algorithms, genetic programming, Evolutionary
computation, Routing, Task analysis, Standards",
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DOI = "doi:10.1109/CEC45853.2021.9504785",
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notes = "Also known as \cite{9504785}",
- }
Genetic Programming entries for
Jordan MacLachlan
Yi Mei
Citations