A Neural Approach to Generation of Constructive Heuristics
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- @InProceedings{Alissa:2021:CEC,
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author = "Mohamad Alissa and Kevin Sim and Emma Hart",
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booktitle = "2021 IEEE Congress on Evolutionary Computation (CEC)",
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title = "A Neural Approach to Generation of Constructive
Heuristics",
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year = "2021",
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editor = "Yew-Soon Ong",
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pages = "1147--1154",
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address = "Krakow, Poland",
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month = "28 " # jun # "-1 " # jul,
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keywords = "genetic algorithms, genetic programming, Measurement,
Navigation, Heuristic algorithms, Neural networks, ANN,
Evolutionary computation, Dynamic scheduling, Automatic
Heuristics Generation, Hyper-Heuristics,
Encoder-Decoder LSTM, Streaming Bin-packing",
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isbn13 = "978-1-7281-8393-0",
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DOI = "doi:10.1109/CEC45853.2021.9504989",
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abstract = "Both algorithm-selection methods and hyper-heuristic
methods rely on a pool of complementary heuristics.
Improving the pool with new heuristics can improve
performance, however, designing new heuristics can be
challenging. Methods such as genetic programming have
proved successful in automating this process in the
past. Typically, these make use of problem
state-information and existing heuristics as
components. Here we propose a novel neural approach for
generating constructive heuristics, in which a neural
network acts as a heuristic by generating decisions. We
evaluate two architectures, an Encoder-Decoder LSTM and
a Feed-Forward Neural Network. Both are trained using
the decisions output from existing heuristics on a
large set of instances. We consider streaming instances
of bin-packing problems in a continual stream that must
be packed immediately in strict order and using a
limited number of resources. We show that the new
heuristics generated are capable of solving a subset of
instances better than the well-known heuristics forming
the original pool, and hence the overall value of the
pool is improved w.r.t. both Falkenauers performance
metric and the number of bins used.",
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notes = "Also known as \cite{9504989}",
- }
Genetic Programming entries for
Mohamad Alissa
Kevin Sim
Emma Hart
Citations