Hyper-heuristic Evolution of Dispatching Rules: A Comparison of Rule Representations

Created by W.Langdon from gp-bibliography.bib Revision:1.3963

@Article{Branke:2015:EC,
  author =       "Juergen Branke and Torsten Hildebrandt and 
                 Bernd Scholz-Reiter",
  title =        "Hyper-heuristic Evolution of Dispatching Rules: A
                 Comparison of Rule Representations",
  journal =      "Evolutionary Computation",
  year =         "2015",
  volume =       "23",
  number =       "2",
  pages =        "249--277",
  month =        "Summer",
  keywords =     "genetic algorithms, genetic programming, Job Shop
                 Scheduling, Dispatching Rule, Representation, CMA-ES,
                 Artificial Neural Network",
  ISSN =         "1063-6560",
  DOI =          "doi:10.1162/EVCO_a_00131",
  size =         "29 pages",
  abstract =     "Dispatching rules are frequently used for real-time,
                 on-line scheduling in complex manufacturing systems.
                 Design of such rules is usually done by experts in a
                 time consuming trial-and-error process. Recently,
                 evolutionary algorithms have been proposed to automate
                 the design process. There are several possibilities to
                 represent rules for this hyper-heuristic search.
                 Because the representation determines the search
                 neighbourhood and the complexity of the rules that can
                 be evolved, a suitable choice of representation is key
                 for a successful evolutionary algorithm. In this paper
                 we empirically compare three different representations,
                 both numeric and symbolic, for automated rule design: A
                 linear combination of attributes, a representation
                 based on Artificial Neural Networks, and a tree
                 representation. Using appropriate Evolutionary
                 Algorithms (CMA-ES for the Neural Network and linear
                 representations, Genetic Programming for the tree
                 representation), we empirically investigate the
                 suitability of each representation in a dynamic
                 stochastic job shop scenario. We also examine the
                 robustness of the evolved dispatching rules against
                 variations in the underlying job shop scenario, and
                 visualise what the rules do in order to get an
                 intuitive understanding of their inner workings.
                 Results indicate that the tree representation using an
                 improved version of Genetic Programming gives the best
                 results if many candidate rules can be evaluated,
                 closely followed by the Neural Network representation
                 that leads to good results already for small to
                 moderate computational budgets. The linear
                 representation is found to be competitive only for
                 extremely small computational budgets.",
  notes =        "Warwick Business School, University of Warwick, CV4
                 7AL Coventry, UK",
}

Genetic Programming entries for Jurgen Branke Torsten Hildebrandt Bernd Scholz-Reiter

Citations