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

A1 Originalartikel i en vetenskaplig tidskrift (referentgranskad)


Interna författare/redaktörer


Publikationens författare: Brijesh Kumar Giri, Frank Pettersson, Henrik Saxén, Nirupam Chakraborti
Förläggare: TAYLOR & FRANCIS INC
Publiceringsår: 2013
Tidskrift: Materials and Manufacturing Processes
Tidskriftsakronym: MATER MANUF PROCESS
Volym: 28
Nummer: 7
Artikelns första sida, sidnummer: 776
Artikelns sista sida, sidnummer: 782
Antal sidor: 7
ISSN: 1042-6914
eISSN: 1532-2475


Abstrakt

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.


Nyckelord

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

Senast uppdaterad 2020-29-01 vid 07:47