The Automatic Design of Hyper-heuristic Framework with Gene Expression Programming for Combinatorial Optimization problems

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@Article{Sabar:2014:ieeeTEC,
  author =       "Nasser R. Sabar and Masri Ayob and Graham Kendall and 
                 Rong Qu",
  title =        "The Automatic Design of Hyper-heuristic Framework with
                 Gene Expression Programming for Combinatorial
                 Optimization problems",
  journal =      "IEEE Transactions on Evolutionary Computation",
  year =         "2015",
  volume =       "19",
  number =       "3",
  pages =        "309--325",
  month =        jun,
  keywords =     "genetic algorithms, genetic programming, Gene
                 Expression Programming, Timetabling, Vehicle Routing,
                 Dynamic Optimisation",
  ISSN =         "1089-778X",
  DOI =          "doi:10.1109/TEVC.2014.2319051",
  size =         "18 pages",
  abstract =     "Hyper-heuristic approaches aim to automate heuristic
                 design in order to solve multiple problems instead of
                 designing tailor-made methodologies for individual
                 problems. Hyper-heuristics accomplish this through a
                 high level heuristic (heuristic selection mechanism and
                 an acceptance criterion). This automates heuristic
                 selection, deciding whether to accept or reject the
                 returned solution. The fact that different problems or
                 even instances, have different landscape structures and
                 complexity, the design of efficient high level
                 heuristics can have a dramatic impact on
                 hyper-heuristic performance. In this work, instead of
                 using human knowledge to design the high level
                 heuristic, we propose a gene expression programming
                 algorithm to automatically generate, during the
                 instance solving process, the high level heuristic of
                 the hyper-heuristic framework. The generated heuristic
                 takes information (such as the quality of the generated
                 solution and the improvement made) from the current
                 problem state as input and decides which low level
                 heuristic should be selected and the acceptance or
                 rejection of the resultant solution. The benefit of
                 this framework is the ability to generate, for each
                 instance, different high level heuristics during the
                 problem solving process. Furthermore, in order to
                 maintain solution diversity, we use a memory mechanism
                 which contains a population of both high quality and
                 diverse solutions that is updated during the problem
                 solving process. The generality of the proposed
                 hyper-heuristic is validated against six well known
                 combinatorial optimisation problem, with very different
                 landscapes, provided by the HyFlex software. Empirical
                 results comparing the proposed hyper-heuristic with
                 state of the art hyper-heuristics, conclude that the
                 proposed hyper-heuristic generalises well across all
                 domains and achieves competitive, if not superior,
                 results for several instances on all domains.",
  notes =        "N. R. Sabar is with Data Mining and Optimisation
                 Research Group (DMO), University Kebangsaan Malaysia,
                 UKM Bangi 43600, Selangor, Malaysia, and also with the
                 University of Nottingham Malaysia Campus, Jalan Broga,
                 Semenyih 43500, Selangor, Malaysia.

                 also known as \cite{6805577}",
}

Genetic Programming entries for Nasser R Sabar Masri Ayob Graham Kendall Rong Qu

Citations