Multi-stage genetic programming: A new strategy to nonlinear system modeling

Created by W.Langdon from gp-bibliography.bib Revision:1.3872

@Article{Gandomi20115227,
  author =       "Amir Hossein Gandomi and Amir Hossein Alavi",
  title =        "Multi-stage genetic programming: A new strategy to
                 nonlinear system modeling",
  journal =      "Information Sciences",
  volume =       "181",
  number =       "23",
  pages =        "5227--5239",
  year =         "2011",
  ISSN =         "0020-0255",
  DOI =          "doi:10.1016/j.ins.2011.07.026",
  URL =          "http://www.sciencedirect.com/science/article/pii/S0020025511003586",
  keywords =     "genetic algorithms, genetic programming, Nonlinear
                 system modelling, Engineering problems, Formulation",
  abstract =     "This paper presents a new multi-stage genetic
                 programming (MSGP) strategy for modelling nonlinear
                 systems. The proposed strategy is based on
                 incorporating the individual effect of predictor
                 variables and the interactions among them to provide
                 more accurate simulations. According to the MSGP
                 strategy, an efficient formulation for a problem
                 comprises different terms. In the first stage of the
                 MSGP-based analysis, the output variable is formulated
                 in terms of an influencing variable. Thereafter, the
                 error between the actual and the predicted value is
                 formulated in terms of a new variable. Finally, the
                 interaction term is derived by formulating the
                 difference between the actual values and the values
                 predicted by the individually developed terms. The
                 capabilities of MSGP are illustrated by applying it to
                 the formulation of different complex engineering
                 problems. The problems analysed herein include the
                 following: (i) simulation of pH neutralisation process,
                 (ii) prediction of surface roughness in end milling,
                 and (iii) classification of soil liquefaction
                 conditions. The validity of the proposed strategy is
                 confirmed by applying the derived models to the parts
                 of the experimental results that were not included in
                 the analyses. Further, the external validation of the
                 models is verified using several statistical criteria
                 recommended by other researchers. The MSGP-based
                 solutions are capable of effectively simulating the
                 nonlinear behaviour of the investigated systems. The
                 results of MSGP are found to be more accurate than
                 those of standard GP and artificial neural
                 network-based models.",
}

Genetic Programming entries for A H Gandomi A H Alavi

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