Template method hyper-heuristics

Created by W.Langdon from gp-bibliography.bib Revision:1.4549

  author =       "John R. Woodward and Jerry Swan",
  title =        "Template method hyper-heuristics",
  booktitle =    "GECCO 2014 Workshop on Metaheuristic Design Patterns
  year =         "2014",
  editor =       "Jerry Swan and Krzysztof Krawiec and John Woodward and 
                 Chris Simons and John Clark",
  isbn13 =       "978-1-4503-2881-4",
  keywords =     "genetic algorithms, genetic programming,
  pages =        "1437--1438",
  month =        "12-16 " # jul,
  organisation = "SIGEVO",
  address =      "Vancouver, BC, Canada",
  URL =          "http://doi.acm.org/10.1145/2598394.2609843",
  DOI =          "doi:10.1145/2598394.2609843",
  publisher =    "ACM",
  publisher_address = "New York, NY, USA",
  acmid =        "2609843",
  size =         "2 pages",
  abstract =     "The optimisation literature is awash with
                 metaphorically-inspired meta-heuristics and their
                 subsequent variants and hybridisation. This results in
                 a plethora of methods, with descriptions that are often
                 polluted with the language of the metaphor which
                 inspired them [8]. Within such a fragmented field, the
                 traditional approach of manual 'operator tweaking'
                 makes it difficult to establish the contribution of
                 individual metaheuristic components to the overall
                 success of a methodology.

                 Irrespective of whether it happens to best the
                 state-of-the-art, such 'tweaking' is so
                 labour-intensive that does relatively little to advance
                 scientific understanding. In order to introduce further
                 structure and rigour, it is therefore desirable to not
                 only to be able to specify entire families of
                 metaheuristics (rather than individual metaheuristics),
                 but also be able to generate and test them. In
                 particular, the adoption of a model agnostic approach
                 towards the generation of metaheuristics would help to
                 establish which metaheuristic components are useful
                 contributors to a solution.",
  notes =        "Also known as \cite{2609843}
                 \cite{Woodward:2014:TMH:2598394.2609843} Distributed at

Genetic Programming entries for John R Woodward Jerry Swan