Hyper-heuristics: a survey of the state of the art

Created by W.Langdon from gp-bibliography.bib Revision:1.3973

@Article{Burke2013,
  author =       "Edmund K Burke and Michel Gendreau and 
                 Matthew Hyde and Graham Kendall and Gabriela Ochoa and 
                 Ender Ozcan and Rong Qu",
  title =        "Hyper-heuristics: a survey of the state of the art",
  journal =      "Journal of the Operational Research Society",
  year =         "2013",
  volume =       "64",
  number =       "12",
  pages =        "1695--1724",
  month =        dec,
  keywords =     "genetic algorithms, genetic programming,
                 Hyper-heuristics, evolutionary computation,
                 metaheuristics, machine learning, combinatorial
                 optimisation, scheduling",
  publisher =    "Palgrave Macmillan",
  ISSN =         "0160-5682",
  URL =          "http://www.cs.nott.ac.uk/~rxq/files/HHSurveyJORS2013.pdf",
  DOI =          "doi:10.1057/jors.2013.71",
  size =         "20 pages",
  abstract =     "Hyper-heuristics comprise a set of approaches that are
                 motivated (at least in part) by the goal of automating
                 the design of heuristic methods to solve hard
                 computational search problems. An underlying strategic
                 research challenge is to develop more generally
                 applicable search methodologies. The term
                 hyper-heuristic is relatively new, it was first used in
                 2000 to describe heuristics to choose heuristics in the
                 context of combinatorial optimisation. However, the
                 idea of automating the design of heuristics is not new;
                 it can be traced back to the 1960s. The definition of
                 hyper-heuristics has been recently extended to refer to
                 a search method or learning mechanism for selecting or
                 generating heuristics to solve computational search
                 problems. Two main hyper-heuristic categories can be
                 considered: heuristic selection and heuristic
                 generation. The distinguishing feature of
                 hyper-heuristics is that they operate on a search space
                 of heuristics (or heuristic components) rather than
                 directly on the search space of solutions to the
                 underlying problem that is being addressed. This paper
                 presents a critical discussion of the scientific
                 literature on hyper-heuristics including their origin
                 and intellectual roots, a detailed account of the main
                 types of approaches, and an overview of some related
                 areas. Current research trends and directions for
                 future research are also discussed.",
  notes =        "several listed approaches use GP",
}

Genetic Programming entries for Edmund Burke Michel Gendreau Matthew R Hyde Graham Kendall Gabriela Ochoa Ender Ozcan Rong Qu

Citations