MGP–CC: a hybrid multigene GP–Cuckoo search method for hot rolling manufacture process modelling

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  author =       "Hossam Faris and Alaa F. Sheta and Ertan Oznergiz",
  title =        "{MGP–CC}: a hybrid multigene {GP–Cuckoo} search
                 method for hot rolling manufacture process modelling",
  journal =      "Systems Science \& Control Engineering",
  year =         "2016",
  volume =       "4",
  number =       "1",
  pages =        "39--49",
  keywords =     "genetic algorithms, genetic programming, Artificial
                 intelligence, internal model control, intelligent
                 control, manufacturing",
  publisher =    "Taylor and Francis",
  DOI =          "doi:10.1080/21642583.2015.1124032",
  abstract =     "Maintaining high level of quality in hot rolling
                 manufacturing processes is very challenging problem to
                 keep competitiveness in the iron and steel industrial
                 market. Monitoring the quality of the output product
                 helps enhancing the product outcomes, increase the
                 company profit and improve the economic growth of the
                 country. In this paper, we propose a new hybrid
                 approach based on multigene genetic programming (MGP)
                 and Cuckoo search (CS) algorithms for developing three
                 rigorous models for roll force, torque and slab
                 temperature in the hot rolling industrial process at
                 the Ereg~li Iron and Steel Factory in Turkey. MGP is a
                 robust variation of the standard genetic programming
                 (GP) algorithm while CS is a new nature-inspired
                 metaheuristic search algorithm. The performance of the
                 developed models is evaluated and compared with those
                 obtained for the standard MGP and GP approaches.",

Genetic Programming entries for Hossam Faris Alaa Sheta Ertan Oznergiz