Improving Job Shop Dispatching Rules via Terminal Weighting and Adaptive Mutation in Genetic Programming

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

@InProceedings{Riley:2016:CEC,
  author =       "Michael Riley and Yi Mei and Mengjie Zhang",
  title =        "Improving Job Shop Dispatching Rules via Terminal
                 Weighting and Adaptive Mutation in Genetic
                 Programming",
  booktitle =    "Proceedings of 2016 IEEE Congress on Evolutionary
                 Computation (CEC 2016)",
  year =         "2016",
  editor =       "Yew-Soon Ong",
  pages =        "3362--3369",
  address =      "Vancouver",
  month =        "24-29 " # jul,
  publisher =    "IEEE Press",
  keywords =     "genetic algorithms, genetic programming",
  isbn13 =       "978-1-5090-0623-6",
  DOI =          "doi:10.1109/CEC.2016.7744215",
  abstract =     "Automatic design of dispatching rules with Genetic
                 Programming (GP) in job shop scheduling has become more
                 prevalent in recent years. When evolving dispatching
                 rules, choosing a proper terminal set is an important
                 issue. There are a large number of attributes in the
                 job shop that can be taken into account as terminals.
                 However, not all of them are useful to be included. It
                 is not a trivial task to identify the most important
                 attributes out of the entire attribute pool. On the
                 other hand, including all the attributes in the
                 terminal set leads to a huge search space for GP, and
                 makes it hard to find the promising regions of the
                 search space. In this paper, we first demonstrate the
                 differences in importance of attributes by frequency
                 analysis. Then, we propose a terminal weighting
                 algorithm to learn the importance of the terminals
                 on-the-fly, and an adaptive mutation scheme to guide
                 the search to concentrate on the more important
                 terminals. The experimental studies show that the
                 proposed algorithm outperformed its counterpart without
                 terminal weighting and adaptive mutation, in the tested
                 dynamic job shop scheduling, while optimising the mean
                 weighted tardiness. This verifies that focusing on the
                 important terminals will help to search inside more
                 promising regions and lead to better solutions.",
  notes =        "WCCI2016",
}

Genetic Programming entries for Michael Riley Yi Mei Mengjie Zhang

Citations