MGP-CC: a hybrid multigene GP-Cuckoo search method for hot rolling manufacture process modelling
Created by W.Langdon from
gp-bibliography.bib Revision:1.8098
- @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