Grammatical Evolution Hyper-Heuristic for Combinatorial Optimization Problems
Created by W.Langdon from
gp-bibliography.bib Revision:1.8081
- @Article{Sabar:2013:ieeeTEC,
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author = "Nasser R. Sabar and Masri Ayob and Graham Kendall and
Rong Qu",
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title = "Grammatical Evolution Hyper-Heuristic for
Combinatorial Optimization Problems",
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journal = "IEEE Transactions on Evolutionary Computation",
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year = "2013",
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volume = "17",
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number = "6",
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pages = "840--861",
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month = dec,
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keywords = "genetic algorithms, genetic programming, Grammatical
Evolution",
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ISSN = "1089-778X",
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DOI = "doi:10.1109/TEVC.2013.2281527",
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size = "22 pages",
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abstract = "Designing generic problem solvers that perform well
across a diverse set of problems is a challenging task.
In this work, we propose a hyper-heuristic framework to
automatically generate an effective and generic
solution method by using grammatical evolution. In the
proposed framework, grammatical evolution is used as an
online solver builder, which takes several heuristic
components (e.g., different acceptance criteria and
different neighbourhood structures) as inputs and
evolves templates of perturbation heuristics. The
evolved templates are improvement heuristics, which
represent a complete search method to solve the problem
at hand. To test the generality and the performance of
the proposed method, we consider two well-known
combinatorial optimisation problems: exam timetabling
(Carter and ITC 2007 instances) and the capacitated
vehicle routing problem (Christofides and Golden
instances). We demonstrate that the proposed method is
competitive, if not superior, when compared to
state-of-the-art hyper-heuristics, as well as bespoke
methods for these different problem domains. In order
to further improve the performance of the proposed
framework we use an adaptive memory mechanism, which
contains a collection of both high quality and diverse
solutions and is updated during the problem solving
process. Experimental results show that the grammatical
evolution hyper-heuristic, with an adaptive memory,
performs better than the grammatical evolution
hyper-heuristic without a memory. The improved
framework also outperforms some bespoke methodologies,
which have reported best known results for some
instances in both problem domains.",
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notes = "also known as \cite{6595625}",
- }
Genetic Programming entries for
Nasser R Sabar
Masri Ayob
Graham Kendall
Rong Qu
Citations