A genetic programming hyper-heuristic: Turning features into heuristics for constraint satisfaction
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- @InProceedings{Ortiz-Bayliss:2013:UKCI,
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author = "Jose Carlos Ortiz-Bayliss and Ender Ozcan and
Andrew J. Parkes and Hugo Terashima-Marin",
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booktitle = "13th UK Workshop on Computational Intelligence (UKCI
2013)",
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title = "A genetic programming hyper-heuristic: Turning
features into heuristics for constraint satisfaction",
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year = "2013",
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month = "9-11 " # sep,
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pages = "183--190",
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address = "Guildford",
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publisher = "IEEE",
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keywords = "genetic algorithms, genetic programming, constraint
satisfaction problems",
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DOI = "doi:10.1109/UKCI.2013.6651304",
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abstract = "A constraint satisfaction problem (CSP) is a
combinatorial optimisation problem with many real world
applications. One of the key aspects to consider when
solving a CSP is the order in which the variables are
selected to be instantiated. In this study, we describe
a genetic programming hyper-heuristic approach to
automatically produce heuristics for CSPs.
Human-designed `standard' heuristics are used as
components enabling the construction of new variable
ordering heuristics which is achieved through the
proposed approach. We present empirical evidence that
the heuristics produced by our approach are competitive
considering the cost of the search when compared to the
standard heuristics which are used to obtain the
components for the new heuristics. The proposed
approach is able to produce specialised heuristics for
specific classes of instances that outperform the best
standard heuristics for the same instances.",
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notes = "Also known as \cite{6651304}",
- }
Genetic Programming entries for
Jose Carlos Ortiz-Bayliss
Ender Ozcan
Andrew J Parkes
Hugo Terashima-Marin
Citations