Enhancing Genetic Programming based Hyper-Heuristics for Dynamic Multi-objective Job Shop Scheduling Problems

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

@InProceedings{Nguyen:2015:CEC,
  author =       "Su Nguyen and Mengjie Zhang and Kay Chen Tan",
  title =        "Enhancing Genetic Programming based Hyper-Heuristics
                 for Dynamic Multi-objective Job Shop Scheduling
                 Problems",
  booktitle =    "Proceedings of 2015 IEEE Congress on Evolutionary
                 Computation (CEC 2015)",
  year =         "2015",
  editor =       "Yadahiko Murata",
  pages =        "2781--2788",
  address =      "Sendai, Japan",
  month =        "25-28 " # may,
  publisher =    "IEEE Press",
  keywords =     "genetic algorithms, genetic programming",
  DOI =          "doi:10.1109/CEC.2015.7257234",
  abstract =     "Genetic programming based hyper-heuristics have been
                 an suitable approach to designing powerful dispatching
                 rules for dynamic job shop scheduling. However, most
                 current methods only focus on a single objective while
                 practical problems almost always involve multiple
                 conflicting objectives. Some efforts have been made to
                 design non-dominated dispatching rules but using
                 genetic programming to deal with multiple objectives is
                 still very challenging because of the large search
                 space and the stochastic characteristics of job shops.
                 This paper investigates different strategies to use
                 computational budgets when evolving dispatching rules
                 with genetic programming. The results suggest that
                 using local search heuristics can enhance the quality
                 of evolved dispatching rules. Moreover, the results
                 show that there are some differences in evolving rules
                 for single objectives and for multiple objectives and
                 that it is difficult to efficiently estimate the Pareto
                 dominance of rules.",
  notes =        "1435 hrs 15585 CEC2015",
}

Genetic Programming entries for Su Nguyen Mengjie Zhang Kay Chen Tan

Citations