Application of genetic programming in analyzing multiple steady states of dynamical systems

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

  author =       "Ming-Yi Lay",
  title =        "Application of genetic programming in analyzing
                 multiple steady states of dynamical systems",
  booktitle =    "Proceedings of the 1994 IEEE World Congress on
                 Computational Intelligence",
  year =         "1994",
  volume =       "1",
  pages =        "333--336b",
  address =      "Orlando, Florida, USA",
  month =        "27-29 " # jun,
  publisher =    "IEEE Press",
  keywords =     "genetic algorithms, genetic programming, Hopf
                 bifurcation points, dynamical systems, genetic
                 programming paradigm, multiple steady states,
                 bifurcation, linear programming, search problems",
  DOI =          "doi:10.1109/ICEC.1994.349930",
  size =         "6 pages",
  abstract =     "Multiple steady states are very interesting phenomena
                 in dynamical systems. However, it is hard to analyse
                 these kinds of phenomena directly by using traditional
                 numerical methods. It is shown that the genetic
                 programming paradigm could be used to directly analyze
                 the existence of multiple steady states in dynamical
                 systems and it could even possibly be applied in
                 analysing other kinds of behaviour in dynamical
                 systems, e.g., the Hopf bifurcation points",
  notes =        "Uses GP to search for steady states in a reaction
                 vessel. The equations for the behaviour of the
                 chemicals is known but not how to solve them. GP is
                 able to find to high accuracy (7 figure) the steady
                 states. States are divined by two floating point
                 variables. Each represented within the prog by an
                 effectively independent tree, ie they don't exchange
                 via crossover.",

Genetic Programming entries for Ming-Yi Lay