Genetic programming through bi-objective genetic algorithms with a study of a simulated moving bed process involving multiple objectives

Created by W.Langdon from gp-bibliography.bib Revision:1.3963

@Article{Giri:2013:ASC,
  author =       "Brijesh Kumar Giri and Jussi Hakanen and 
                 Kaisa Miettinen and Nirupam Chakraborti",
  title =        "Genetic programming through bi-objective genetic
                 algorithms with a study of a simulated moving bed
                 process involving multiple objectives",
  journal =      "Applied Soft Computing",
  year =         "2013",
  volume =       "13",
  number =       "5",
  pages =        "2613--2623",
  month =        may,
  keywords =     "genetic algorithms, genetic programming, Evolutionary
                 algorithms, Neural networks, ANN, Multi-objective
                 optimisation, MOGP, Computational cost, Meta-models,
                 Simulation-based optimisation",
  ISSN =         "1568-4946",
  URL =          "http://www.sciencedirect.com/science/article/pii/S1568494612005091",
  DOI =          "doi:10.1016/j.asoc.2012.11.025",
  size =         "11 pages",
  abstract =     "A new bi-objective genetic programming (BioGP)
                 technique has been developed for meta-modelling and
                 applied in a chromatographic separation process using a
                 simulated moving bed (SMB) process. The BioGP technique
                 initially minimises training error through a single
                 objective optimisation procedure and then a trade-off
                 between complexity and accuracy is worked out through a
                 genetic algorithm based bi-objective optimization
                 strategy. A benefit of the BioGP approach is that an
                 expert user or a decision maker (DM) can flexibly
                 select the mathematical operations involved to
                 construct a meta-model of desired complexity or
                 accuracy. It is also designed to combat bloat, a
                 perennial problem in genetic programming along with
                 over fitting and under fitting problems. In this study
                 the meta-models constructed for SMB reactors were
                 compared with those obtained from an evolutionary
                 neural network (EvoNN) developed earlier and also with
                 a polynomial regression model. Both BioGP and EvoNN
                 were compared for subsequent constrained bi-objective
                 optimization studies for the SMB reactor involving four
                 objectives. The results were also compared with the
                 previous work in the literature. The BioGP technique
                 produced acceptable results and is now ready for
                 data-driven modelling and optimization studies at
                 large.",
}

Genetic Programming entries for Brijesh Kumar Giri Jussi Hakanen Kaisa Miettinen Nirupam Chakraborti

Citations