Grammar-based generation of stochastic local search heuristics through automatic algorithm configuration tools

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@Article{Mascia:2014:COR,
  author =       "Franco Mascia and Manuel Lopez-Ibanez and 
                 Jeremie Dubois-Lacoste and Thomas Stuetzle",
  title =        "Grammar-based generation of stochastic local search
                 heuristics through automatic algorithm configuration
                 tools",
  journal =      "Computer \& Operations Research",
  volume =       "51",
  pages =        "190--199",
  year =         "2014",
  keywords =     "genetic algorithms, genetic programming, Heuristics,
                 Grammatical evolution, Automatic algorithm
                 configuration, Bin packing, Flowshop scheduling",
  ISSN =         "0305-0548",
  DOI =          "doi:10.1016/j.cor.2014.05.020",
  URL =          "http://www.sciencedirect.com/science/article/pii/S0305054814001555",
  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