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

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@Article{faris2016mgp,

• 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

Citations