A Genetic Programming Hyper-Heuristic for the Multidimensional Knapsack Problem

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

  author =       "John H. Drake and Matthew Hyde and Khaled Ibrahim and 
                 Ender Ozcan",
  title =        "A Genetic Programming Hyper-Heuristic for the
                 Multidimensional Knapsack Problem",
  year =         "2012",
  booktitle =    "11th IEEE International Conference on Cybernetic
                 Intelligent Systems",
  editor =       "N. H. Siddique and Michael O'Grady",
  address =      "Limerick, Ireland",
  month =        "23-24 " # aug,
  organisation = "IEEE Systems, Man and Cybernetics Society with the
                 theme of Cybernetic Intelligent Systems",
  keywords =     "genetic algorithms, genetic programming,
                 hyper-heuristics, heuristic generation,
                 multidimensional knapsack problem",
  annote =       "The Pennsylvania State University CiteSeerX Archives",
  bibsource =    "OAI-PMH server at citeseerx.ist.psu.edu",
  language =     "en",
  oai =          "oai:CiteSeerX.psu:",
  URL =          "http://citeseerx.ist.psu.edu/viewdoc/summary?doi=",
  URL =          "http://www.cs.nott.ac.uk/~exo/docs/publications/cis2012_GP_MKP.pdf",
  size =         "5 pages",
  abstract =     "Hyper-heuristics are a class of high-level search
                 techniques which operate on a search space of
                 heuristics rather than directly on a search space of
                 solutions. Early hyperheuristics focused on selecting
                 and applying a low-level heuristic at each stage of a
                 search. Recent trends in hyper-heuristic research have
                 led to a number of approaches being developed to
                 automatically generate new heuristics from a set of
                 heuristic components. This work investigates the
                 suitability of using genetic programming as a
                 hyper-heuristic methodology to generate constructive
                 heuristics to solve the multidimensional 0-1 knapsack
                 problem. A population of heuristics to rank knapsack
                 items are trained on a subset of test problems and then
                 applied to unseen instances. The results over a set of
                 standard benchmarks show that genetic programming can
                 be used to generate constructive heuristics which yield
                 human-competitive results.",
  notes =        "May 2014 not in IEEE


Genetic Programming entries for John H Drake Matthew R Hyde Khaled Ibrahim Ender Ozcan