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

Brijesh Kumar Giri; Frank Pettersson; Henrik Saxén; Nirupam Chakraborti

doi:10.1080/10426914.2013.763953

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

Brijesh Kumar Giri, Frank Pettersson, Henrik Saxén, Nirupam Chakraborti

Research output: Contribution to journal › Article › Scientific › peer-review

69 Citations (Scopus)

Abstract

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.

Original language	Undefined/Unknown
Pages (from-to)	776–782
Number of pages	7
Journal	Materials and Manufacturing Processes
Volume	28
Issue number	7
DOIs	https://doi.org/10.1080/10426914.2013.763953
Publication status	Published - 2013
MoE publication type	A1 Journal article-refereed

Keywords

Blast furnace
Evolutionary computation
Genetic algorithm
Multiobjective optimization
Neural network
Pareto

Access to Document

10.1080/10426914.2013.763953

http://www.tandfonline.com/doi/abs/10.1080/10426914.2013.763953

Cite this

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title = "Genetic programming evolved through bi-objective genetic algorithms applied to a blast furnace",

abstract = "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.",

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AU - Saxén, Henrik

AU - Chakraborti, Nirupam

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KW - Multiobjective optimization

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