TY - GEN
T1 - Evolutionary optimization of regression model ensembles in steel-making process
AU - Kordos, Miroslaw
AU - Blachnik, Marcin
AU - Wieczorek, Tadeusz
PY - 2011
Y1 - 2011
N2 - In this paper we compare different evolutionary algorithm approaches and parameters used to optimize the output of neural network committee trained on regression problems. This is especially useful for large and complex datasets. We used the methodology presented in this paper to optimize the output of the committee to predict the temperature in the electric arc furnace in one of the steelworks.
AB - In this paper we compare different evolutionary algorithm approaches and parameters used to optimize the output of neural network committee trained on regression problems. This is especially useful for large and complex datasets. We used the methodology presented in this paper to optimize the output of the committee to predict the temperature in the electric arc furnace in one of the steelworks.
KW - evolutionary algorithms
KW - neural network committee
UR - https://www.scopus.com/pages/publications/80053041594
U2 - 10.1007/978-3-642-23878-9_44
DO - 10.1007/978-3-642-23878-9_44
M3 - Conference contribution
AN - SCOPUS:80053041594
SN - 9783642238772
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 369
EP - 376
BT - Intelligent Data Engineering and Automated Learning, IDEAL 2011 - 12th International Conference, Proceedings
T2 - 12th International Conference on Intelligent Data Engineering and Automated Learning, IDEAL 2011
Y2 - 7 September 2011 through 9 September 2011
ER -