Adaptive genetic programming for steady-state process modeling

Created by W.Langdon from gp-bibliography.bib Revision:1.4448

  author =       "Benyamin Grosman and Daniel R. Lewin",
  title =        "Adaptive genetic programming for steady-state process
  journal =      "Computers \& Chemical Engineering",
  year =         "2004",
  volume =       "28",
  pages =        "2779--2790",
  number =       "12",
  abstract =     "Genetic programming is one of the computer algorithms
                 in the family of evolutionary-computational methods,
                 which have been shown to provide reliable solutions to
                 complex optimisation problems. The genetic programming
                 under discussion in this work relies on tree-like
                 building blocks, and thus supports process modelling
                 with varying structure. This paper, which describes an
                 improved GP to facilitate the generation of
                 steady-state nonlinear empirical models for process
                 analysis and optimization, is an evolution of several
                 works in the field. The key feature of the method is
                 its ability to adjust the complexity of the required
                 model to accurately predict the true process behaviour.
                 The improved GP code incorporates a novel fitness
                 calculation, the optimal creation of new generations,
                 and parameter allocation. The advantages of these
                 modifications are tested against the more commonly used
  owner =        "wlangdon",
  URL =          "",
  month =        "15 " # nov,
  keywords =     "genetic algorithms, genetic programming",
  DOI =          "doi:10.1016/j.compchemeng.2004.09.001",

Genetic Programming entries for Benyamin Grosman Daniel R Lewin