Optimization of nonlinear geological structure mapping using hybrid neuro-genetic techniques

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

@Article{Ganesan20112913,
  author =       "T. Ganesan and P. Vasant and I. Elamvazuthi",
  title =        "Optimization of nonlinear geological structure mapping
                 using hybrid neuro-genetic techniques",
  journal =      "Mathematical and Computer Modelling",
  volume =       "54",
  number =       "11-12",
  pages =        "2913--2922",
  year =         "2011",
  ISSN =         "0895-7177",
  DOI =          "doi:10.1016/j.mcm.2011.07.012",
  URL =          "http://www.sciencedirect.com/science/article/pii/S0895717711004225",
  keywords =     "genetic algorithms, genetic programming, Nonlinear,
                 Engineering problems, Geological structure mapping,
                 Hybrid optimisation",
  abstract =     "A fairly reasonable result was obtained for nonlinear
                 engineering problems using the optimisation techniques
                 such as neural network, genetic algorithms, and fuzzy
                 logic independently in the past. Increasingly, hybrid
                 techniques are being used to solve the nonlinear
                 problems to obtain a better output. This paper
                 discusses the use of neuro-genetic hybrid technique to
                 optimise the geological structure mapping which is
                 known as seismic survey. It involves minimisation of
                 objective function subject to the requirement of
                 geophysical and operational constraints. In this work,
                 the optimization was initially performed using genetic
                 programming, and followed by hybrid neuro-genetic
                 programming approaches. Comparative studies and
                 analysis were then carried out on the optimised
                 results. The results indicate that the hybrid
                 neuro-genetic hybrid technique produced better results
                 compared to the stand-alone genetic programming
                 method.",
}

Genetic Programming entries for Timothy Ganesan Pandian Vasant I Elamvazuthi

Citations