Exploring Hyper-heuristic Methodologies with Genetic Programming

Created by W.Langdon from gp-bibliography.bib Revision:1.4496

  author =       "Edmund K. Burke and Mathew R. Hyde and 
                 Graham Kendall and Gabriela Ochoa and Ender Ozcan and 
                 John R. Woodward",
  title =        "Exploring Hyper-heuristic Methodologies with Genetic
  booktitle =    "Computational Intelligence",
  publisher =    "Springer",
  year =         "2009",
  editor =       "Christine L. Mumford and Lakhmi C. Jain",
  volume =       "1",
  series =       "Intelligent Systems Reference Library",
  chapter =      "6",
  pages =        "177--201",
  keywords =     "genetic algorithms, genetic programming",
  isbn13 =       "978-3-642-01798-8",
  URL =          "http://www.cs.nott.ac.uk/~gxo/papers/ChapterGPasHH09.pdf",
  DOI =          "doi:10.1007/978-3-642-01799-5_6",
  abstract =     "Hyper-heuristics represent a novel search methodology
                 that is motivated by the goal of automating the process
                 of selecting or combining simpler heuristics in order
                 to solve hard computational search problems. An
                 extension of the original hyper-heuristic idea is to
                 generate new heuristics which are not currently known.
                 These approaches operate on a search space of
                 heuristics rather than directly on a search space of
                 solutions to the underlying problem which is the case
                 with most meta-heuristics implementations. In the
                 majority of hyper-heuristic studies so far, a framework
                 is provided with a set of human designed heuristics,
                 taken from the literature, and with good measures of
                 performance in practice. A less well studied approach
                 aims to generate new heuristics from a set of potential
                 heuristic components. The purpose of this chapter is to
                 discuss this class of hyper-heuristics, in which
                 Genetic Programming is the most widely used
                 methodology. A detailed discussion is presented
                 including the steps needed to apply this technique,
                 some representative case studies, a literature review
                 of related work, and a discussion of relevant issues.
                 Our aim is to convey the exciting potential of this
                 innovative approach for automating the heuristic design
  size =         "26 pages",

Genetic Programming entries for Edmund Burke Matthew R Hyde Graham Kendall Gabriela Ochoa Ender Ozcan John R Woodward