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 journalArticleScientificpeer-review

    63 Citations (Scopus)


    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 languageUndefined/Unknown
    Pages (from-to)776–782
    Number of pages7
    JournalMaterials and Manufacturing Processes
    Issue number7
    Publication statusPublished - 2013
    MoE publication typeA1 Journal article-refereed


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

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