Niching Genetic Programming based Hyper-heuristic Approach to Dynamic Job Shop Scheduling: An Investigation into Distance Metrics

Created by W.Langdon from gp-bibliography.bib Revision:1.3872

@InProceedings{Park:2016:GECCOcomp,
  author =       "John Park and Yi Mei and Gang Chen and Mengjie Zhang",
  title =        "Niching Genetic Programming based Hyper-heuristic
                 Approach to Dynamic Job Shop Scheduling: An
                 Investigation into Distance Metrics",
  booktitle =    "GECCO '16 Companion: Proceedings of the Companion
                 Publication of the 2016 Annual Conference on Genetic
                 and Evolutionary Computation",
  year =         "2016",
  editor =       "Tobias Friedrich and Frank Neumann and 
                 Andrew M. Sutton and Martin Middendorf and Xiaodong Li and 
                 Emma Hart and Mengjie Zhang and Youhei Akimoto and 
                 Peter A. N. Bosman and Terry Soule and Risto Miikkulainen and 
                 Daniele Loiacono and Julian Togelius and 
                 Manuel Lopez-Ibanez and Holger Hoos and Julia Handl and 
                 Faustino Gomez and Carlos M. Fonseca and 
                 Heike Trautmann and Alberto Moraglio and William F. Punch and 
                 Krzysztof Krawiec and Zdenek Vasicek and 
                 Thomas Jansen and Jim Smith and Simone Ludwig and JJ Merelo and 
                 Boris Naujoks and Enrique Alba and Gabriela Ochoa and 
                 Simon Poulding and Dirk Sudholt and Timo Koetzing",
  pages =        "109--110",
  keywords =     "genetic algorithms, genetic programming: Poster",
  month =        "20-24 " # jul,
  organisation = "SIGEVO",
  address =      "Denver, USA",
  publisher =    "ACM",
  publisher_address = "New York, NY, USA",
  isbn13 =       "978-1-4503-4323-7",
  DOI =          "doi:10.1145/2908961.2908985",
  abstract =     "investigates the application of fitness sharing to a
                 coevolutionary genetic programming based
                 hyper-heuristic (GP-HH) approach to a dynamic job shop
                 scheduling (DJSS) problem that evolves an ensemble of
                 dispatching rules. Evolving ensembles using GP-HH for
                 DJSS problem is a relatively unexplored area, and has
                 been shown to outperform standard GP-HH procedures that
                 evolve single rules. As a fitness sharing algorithm has
                 not been applied to the specific GP-HH approach, we
                 investigate four different phenotypic distance measures
                 as part of a fitness sharing algorithm. The fitness
                 sharing algorithm may potentially improve the diversity
                 of the constituent members of the ensemble and improve
                 the quality of the ensembles. The results show that the
                 niched co-evolutionary GP approaches evolve smaller
                 sized rules than the base coevolutionary GP approaches,
                 but have similar performances.",
  notes =        "Distributed at GECCO-2016.",
}

Genetic Programming entries for John Park Yi Mei Gang Chen Mengjie Zhang

Citations