Genetic programming evolved through bi-objective genetic algorithms applied to a blast furnace

A1 Journal article (refereed)

Internal Authors/Editors

Publication Details

List of Authors: Brijesh Kumar Giri, Frank Pettersson, Henrik Saxén, Nirupam Chakraborti
Publication year: 2013
Journal: Materials and Manufacturing Processes
Journal acronym: MATER MANUF PROCESS
Volume number: 28
Issue number: 7
Start page: 776
End page: 782
Number of pages: 7
ISSN: 1042-6914
eISSN: 1532-2475


In this study, a new Bi-objective Genetic Programming (BioGP) technique was developed that initially attempts to minimize training error through a single objective procedure and subsequently switches to bi-objective evolution to work out a Pareto-tradeoff between model complexity and accuracy. For a set of highly noisy industrial data from an operational ironmaking blast furnace (BF) this method was pitted against an Evolutionary Neural Network (EvoNN) developed earlier by the authors. The BioGP procedure was found to produce very competitive results for this complex modeling problem and because of its generic nature, opens a new avenue for data-driven modeling in many other domains.


Blast furnace, Evolutionary computation, Genetic algorithm, Multiobjective optimization, Neural network, Pareto

Last updated on 2020-21-09 at 06:13