A Single Population Genetic Programming based Ensemble Learning Approach to Job Shop Scheduling

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

  author =       "John Park and Su Nguyen and Mengjie Zhang and 
                 Mark Johnston",
  title =        "A Single Population Genetic Programming based Ensemble
                 Learning Approach to Job Shop Scheduling",
  booktitle =    "GECCO Companion '15: Proceedings of the Companion
                 Publication of the 2015 Annual Conference on Genetic
                 and Evolutionary Computation",
  year =         "2015",
  editor =       "Sara Silva and Anna I Esparcia-Alcazar and 
                 Manuel Lopez-Ibanez and Sanaz Mostaghim and Jon Timmis and 
                 Christine Zarges and Luis Correia and Terence Soule and 
                 Mario Giacobini and Ryan Urbanowicz and 
                 Youhei Akimoto and Tobias Glasmachers and 
                 Francisco {Fernandez de Vega} and Amy Hoover and Pedro Larranaga and 
                 Marta Soto and Carlos Cotta and Francisco B. Pereira and 
                 Julia Handl and Jan Koutnik and Antonio Gaspar-Cunha and 
                 Heike Trautmann and Jean-Baptiste Mouret and 
                 Sebastian Risi and Ernesto Costa and Oliver Schuetze and 
                 Krzysztof Krawiec and Alberto Moraglio and 
                 Julian F. Miller and Pawel Widera and Stefano Cagnoni and 
                 JJ Merelo and Emma Hart and Leonardo Trujillo and 
                 Marouane Kessentini and Gabriela Ochoa and Francisco Chicano and 
                 Carola Doerr",
  isbn13 =       "978-1-4503-3488-4",
  keywords =     "genetic algorithms, genetic programming: Poster",
  pages =        "1451--1452",
  month =        "11-15 " # jul,
  organisation = "SIGEVO",
  address =      "Madrid, Spain",
  URL =          "http://doi.acm.org/10.1145/2739482.2764651",
  DOI =          "doi:10.1145/2739482.2764651",
  publisher =    "ACM",
  publisher_address = "New York, NY, USA",
  abstract =     "Genetic Programming based hyper-heuristics (GP-HH) for
                 dynamic job shop scheduling (JSS) problems are
                 approaches which aim to address the issue where
                 heuristics are only effective for specific JSS problem
                 domains, and that designing effective heuristics for
                 JSS problems can be difficult. This paper is a
                 preliminary investigation into improving the robustness
                 of heuristics evolved by GP-HH by evolving ensembles of
                 dispatching rules from a single population of GP
                 individuals. The results show that the current approach
                 does not evolve significantly better or more robust
                 rules than a standard GP-HH approach of evolving single
                 constituent rules.",
  notes =        "Also known as \cite{2764651} Distributed at

Genetic Programming entries for John Park Su Nguyen Mengjie Zhang Mark Johnston