Contrasting meta-learning and hyper-heuristic research: the role of evolutionary algorithms

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

  author =       "Gisele L. Pappa and Gabriela Ochoa and 
                 Matthew R. Hyde and Alex A. Freitas and John Woodward and Jerry Swan",
  title =        "Contrasting meta-learning and hyper-heuristic
                 research: the role of evolutionary algorithms",
  journal =      "Genetic Programming and Evolvable Machines",
  year =         "2014",
  volume =       "15",
  number =       "1",
  pages =        "3--35",
  month =        mar,
  keywords =     "genetic algorithms, genetic programming,
                 Hyper-heuristics, Meta-learning, Automated algorithm
  ISSN =         "1389-2576",
  DOI =          "doi:10.1007/s10710-013-9186-9",
  size =         "33 pages",
  abstract =     "The fields of machine meta-learning and
                 hyper-heuristic optimisation have developed mostly
                 independently of each other, although evolutionary
                 algorithms (particularly genetic programming) have
                 recently played an important role in the development of
                 both fields. Recent work in both fields shares a common
                 goal, that of automating as much of the algorithm
                 design process as possible. In this paper we first
                 provide a historical perspective on automated algorithm
                 design, and then we discuss similarities and
                 differences between meta-learning in the field of
                 supervised machine learning (classification) and
                 hyper-heuristics in the field of optimisation. This
                 discussion focuses on the dimensions of the problem
                 space, the algorithm space and the performance measure,
                 as well as clarifying important issues related to
                 different levels of automation and generality in both
                 fields. We also discuss important research directions,
                 challenges and foundational issues in meta-learning and
                 hyper-heuristic research. It is important to emphasise
                 that this paper is not a survey, as several surveys on
                 the areas of meta-learning and hyper-heuristics
                 (separately) have been previously published. The main
                 contribution of the paper is to contrast meta-learning
                 and hyper-heuristics methods and concepts, in order to
                 promote awareness and cross-fertilisation of ideas
                 across the (by and large, non-overlapping) different
                 communities of meta-learning and hyper-heuristic
                 researchers. We hope that this cross-fertilisation of
                 ideas can inspire interesting new research in both
                 fields and in the new emerging research area which
                 consists of integrating those fields.",

Genetic Programming entries for Gisele L Pappa Gabriela Ochoa Matthew R Hyde Alex Alves Freitas John R Woodward Jerry Swan