Feature Selection in Evolving Job Shop Dispatching Rules with Genetic Programming

Created by W.Langdon from gp-bibliography.bib Revision:1.3973

@InProceedings{Mei:2016:GECCO,
  author =       "Yi Mei and Mengjie Zhang and Su Nguyen",
  title =        "Feature Selection in Evolving Job Shop Dispatching
                 Rules with Genetic Programming",
  booktitle =    "GECCO '16: Proceedings of the 2016 Annual Conference
                 on Genetic and Evolutionary Computation",
  year =         "2016",
  editor =       "Tobias Friedrich",
  pages =        "365--372",
  keywords =     "genetic algorithms, genetic programming, Evolutionary
                 Combinatorial Optimization and Metaheuristics",
  month =        "20-24 " # jul,
  organisation = "SIGEVO",
  address =      "Denver, USA",
  publisher =    "ACM",
  publisher_address = "New York, NY, USA",
  isbn13 =       "978-1-4503-4206-3",
  DOI =          "doi:10.1145/2908812.2908822",
  abstract =     "Genetic Programming (GP) has been successfully used to
                 automatically design dispatching rules in job shop
                 scheduling. The goal of GP is to evolve a priority
                 function that will be used to order the waiting jobs at
                 each decision point, and decide the next job to be
                 processed. To this end, the proper terminals (i.e. job
                 shop features) have to be decided. When evolving the
                 priority function, various job shop features can be
                 included in the terminal set. However, not all the
                 features are helpful, and some features are irrelevant
                 to the rule. Including irrelevant features into the
                 terminal set enlarges the search space, and makes it
                 harder to achieve promising areas. Thus, it is
                 important to identify the important features and remove
                 the irrelevant ones to improve the GP-evolved rules.
                 This paper proposes a domain-knowledge-free feature
                 ranking and selection approach. As a result, the
                 terminal set is significantly reduced and only the most
                 important features are selected. The experimental
                 results show that using only the selected features can
                 lead to significantly better GP-evolved rules on both
                 training and unseen test instances.",
  notes =        "Victoria University of Wellington, Hoa Sen
                 University

                 GECCO-2016 A Recombination of the 25th International
                 Conference on Genetic Algorithms (ICGA-2016) and the
                 21st Annual Genetic Programming Conference (GP-2016)",
}

Genetic Programming entries for Yi Mei Mengjie Zhang Su Nguyen

Citations