An Efficient Feature Selection Algorithm for Evolving Job Shop Scheduling Rules With Genetic Programming

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@Article{Mei:2017:ieeeETCI,
  author =       "Yi Mei and Su Nguyen and Bing Xue and Mengjie Zhang",
  journal =      "IEEE Transactions on Emerging Topics in Computational
                 Intelligence",
  title =        "An Efficient Feature Selection Algorithm for Evolving
                 Job Shop Scheduling Rules With Genetic Programming",
  year =         "2017",
  volume =       "1",
  number =       "5",
  pages =        "339--353",
  abstract =     "Automated design of job shop scheduling rules using
                 genetic programming as a hyper-heuristic is an emerging
                 topic that has become more and more popular in recent
                 years. For evolving dispatching rules, feature
                 selection is an important issue for deciding the
                 terminal set of genetic programming. There can be a
                 large number of features, whose importance/relevance
                 varies from one to another. It has been shown that
                 using a promising feature subset can lead to a
                 significant improvement over using all the features.
                 However, the existing feature selection algorithm for
                 job shop scheduling is too slow and inapplicable in
                 practice. In this paper, we propose the first practical
                 feature selection algorithm for job shop scheduling.
                 Our contributions are twofold. First, we develop a
                 Niching-based search framework for extracting a diverse
                 set of good rules. Second, we reduce the complexity of
                 fitness evaluation by using a surrogate model. As a
                 result, the proposed feature selection algorithm is
                 very efficient. The experimental studies show that it
                 takes less than 10percent of the training time of the
                 standard genetic programming training process, and can
                 obtain much better feature subsets than the entire
                 feature set. Furthermore, it can find better feature
                 subsets than the best-so-far feature subset.",
  keywords =     "genetic algorithms, genetic programming",
  DOI =          "doi:10.1109/TETCI.2017.2743758",
  month =        oct,
  notes =        "Also known as \cite{8048081}",
}

Genetic Programming entries for Yi Mei Su Nguyen Bing Xue Mengjie Zhang

Citations