Genetic Programming for Symbolic Regression of Chemical Process Systems

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

@Article{Babu:2007:EL,
  author =       "B. V. Babu and S. Karthik",
  title =        "Genetic Programming for Symbolic Regression of
                 Chemical Process Systems",
  journal =      "Engineering Letters",
  volume =       "14",
  number =       "2",
  year =         "2007",
  pages =        "42--55",
  month =        jun,
  publisher =    "International Association of Engineers",
  keywords =     "genetic algorithms, genetic programming",
  ISSN =         "1816-0948",
  URL =          "http://www.engineeringletters.com/issues_v14/issue_2/EL_14_2_6.pdf",
  URL =          "http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.148.8378",
  oai =          "oai:CiteSeerXPSU:10.1.1.148.8378",
  abstract =     "The novel evolutionary artificial intelligence
                 formalism namely, genetic programming (GP) a branch of
                 genetic algorithms is used to develop mathematical
                 models based on input-output data, instead of
                 conventional regression and neural network modeling
                 techniques which are commonly used for this purpose.
                 This paper summarizes the available MATLAB toolboxes
                 and their features. Glucose to gluconic acid batch
                 bioprocess has been modeled using both GPLAB and hybrid
                 approach of GP and Orthogonal Least Square method (GP
                 OLS). GP OLS which is capable of pruning of trees has
                 generated parsimonious expressions simpler to GPLAB,
                 with high fitness values and low mean square error
                 which is an indicative of the good prediction accuracy.
                 The capability of GP OLS to generate non-linear
                 input-output dynamic systems has been tested using an
                 example of fed-batch bioreactor. The simulation and GP
                 model prediction results indicate GP OLS is an
                 efficient and fast method for predicting the order and
                 structure for non-linear input and output model.",
  notes =        "http://www.engineeringletters.com/",
}

Genetic Programming entries for B V Babu S Karthik

Citations