Grammar-based generation of stochastic local search heuristics through automatic algorithm configuration tools
Created by W.Langdon from
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- @Article{Mascia:2014:COR,
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author = "Franco Mascia and Manuel Lopez-Ibanez and
Jeremie Dubois-Lacoste and Thomas Stuetzle",
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title = "Grammar-based generation of stochastic local search
heuristics through automatic algorithm configuration
tools",
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journal = "Computer \& Operations Research",
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volume = "51",
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pages = "190--199",
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year = "2014",
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keywords = "genetic algorithms, genetic programming, Heuristics,
Grammatical evolution, Automatic algorithm
configuration, Bin packing, Flowshop scheduling",
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ISSN = "0305-0548",
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DOI = "doi:10.1016/j.cor.2014.05.020",
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URL = "http://www.sciencedirect.com/science/article/pii/S0305054814001555",
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abstract = "Several grammar-based genetic programming algorithms
have been proposed in the literature to automatically
generate heuristics for hard optimisation problems.
These approaches specify the algorithmic building
blocks and the way in which they can be combined in a
grammar; the best heuristic for the problem being
tackled is found by an evolutionary algorithm that
searches in the algorithm design space defined by the
grammar. In this work, we propose a novel
representation of the grammar by a sequence of
categorical, integer, and real-valued parameters. We
then use a tool for automatic algorithm configuration
to search for the best algorithm for the problem at
hand. Our experimental evaluation on the
one-dimensional bin packing problem and the permutation
flowshop problem with weighted tardiness objective
shows that the proposed approach produces better
algorithms than grammatical evolution, a
well-established variant of grammar-based genetic
programming. The reasons behind such improvement lie
both in the representation proposed and in the method
used to search the algorithm design space.",
- }
Genetic Programming entries for
Franco Mascia
Manuel Lopez-Ibanez
Jeremie Dubois-Lacoste
Thomas Stuetzle
Citations