The importance of the learning conditions in hyper-heuristics

Created by W.Langdon from gp-bibliography.bib Revision:1.4524

  author =       "Nuno Lourenco and Francisco Baptista Pereira and 
                 Ernesto Costa",
  title =        "The importance of the learning conditions in
  booktitle =    "GECCO '13: Proceeding of the fifteenth annual
                 conference on Genetic and evolutionary computation
  year =         "2013",
  editor =       "Christian Blum and Enrique Alba and Anne Auger and 
                 Jaume Bacardit and Josh Bongard and Juergen Branke and 
                 Nicolas Bredeche and Dimo Brockhoff and 
                 Francisco Chicano and Alan Dorin and Rene Doursat and 
                 Aniko Ekart and Tobias Friedrich and Mario Giacobini and 
                 Mark Harman and Hitoshi Iba and Christian Igel and 
                 Thomas Jansen and Tim Kovacs and Taras Kowaliw and 
                 Manuel Lopez-Ibanez and Jose A. Lozano and Gabriel Luque and 
                 John McCall and Alberto Moraglio and 
                 Alison Motsinger-Reif and Frank Neumann and Gabriela Ochoa and 
                 Gustavo Olague and Yew-Soon Ong and 
                 Michael E. Palmer and Gisele Lobo Pappa and 
                 Konstantinos E. Parsopoulos and Thomas Schmickl and Stephen L. Smith and 
                 Christine Solnon and Thomas Stuetzle and El-Ghazali Talbi and 
                 Daniel Tauritz and Leonardo Vanneschi",
  isbn13 =       "978-1-4503-1963-8",
  pages =        "1525--1532",
  keywords =     "genetic algorithms, genetic programming",
  month =        "6-10 " # jul,
  organisation = "SIGEVO",
  address =      "Amsterdam, The Netherlands",
  DOI =          "doi:10.1145/2463372.2463558",
  publisher =    "ACM",
  publisher_address = "New York, NY, USA",
  abstract =     "Evolutionary Algorithms are problem solvers inspired
                 by nature. The effectiveness of these methods on a
                 specific task usually depends on a non trivial manual
                 crafting of their main components and settings.
                 Hyper-Heuristics is a recent area of research that aims
                 to overcome this limitation by advocating the
                 automation of the optimisation algorithm design task.
                 In this paper, we describe a Grammatical Evolution
                 framework to automatically design evolutionary
                 algorithms to solve the knapsack problem. We focus our
                 attention on the evaluation of solutions that are
                 iteratively generated by the Hyper-Heuristic. When
                 learning optimisation strategies, the hyper-method must
                 evaluate promising candidates by executing them.
                 However, running an evolutionary algorithm is an
                 expensive task and the computational budget assigned to
                 the evaluation of solutions must be limited. We present
                 a detailed study that analyses the effect of the
                 learning conditions on the optimisation strategies
                 evolved by the Hyper-Heuristic framework. Results show
                 that the computational budget allocation impacts the
                 structure and quality of the learnt architectures. We
                 also present experimental results showing that the best
                 learnt strategies are competitive with state-of-the-art
                 hand designed algorithms in unseen instances of the
                 knapsack problem.",
  notes =        "Also known as \cite{2463558} GECCO-2013 A joint
                 meeting of the twenty second international conference
                 on genetic algorithms (ICGA-2013) and the eighteenth
                 annual genetic programming conference (GP-2013)",

Genetic Programming entries for Nuno Lourenco Francisco Jose Baptista Pereira Ernesto Costa