A regressive schema theory based tool for GP evolved nonlinear models

Created by W.Langdon from gp-bibliography.bib Revision:1.4192

  author =       "Alina Patelli and Lavinia Ferariu",
  title =        "A regressive schema theory based tool for GP evolved
                 nonlinear models",
  booktitle =    "17th International Conference on Automation and
                 Computing (ICAC 2011)",
  year =         "2011",
  month =        "10 " # sep,
  pages =        "201--206",
  address =      "Huddersfield, UK",
  keywords =     "genetic algorithms, genetic programming, GP,
                 multi-objective, evolved nonlinear model, fuzzy
  isbn13 =       "978-1-4673-0000-1",
  URL =          "http://ieeexplore.ieee.org/xpl/articleDetails.jsp?tp=&arnumber=6084927",
  size =         "6 pages",
  abstract =     "Nonlinear systems identification is approached by
                 employing a genetic programming computational tool
                 featuring explicit building block exploitation. The
                 level of adaptation of recurrent model sub-structures
                 is assessed by a fuzzy module. The first contribution
                 of the paper resides in using the fuzzy classification
                 results to reconfigure the cut point selection
                 probabilities of regressor inner nodes, a process
                 called encapsulation. This allows for the second
                 innovation, namely the design of context aware genetic
                 operators capable of protecting the existing instances
                 of fit building blocks and of creating new ones. The
                 computational costs of encapsulation are reduced by
                 employing a novel regressive schema theory - the third
                 and main paper contribution - which assesses the
                 inherent chances of regressor survival. A thorough
                 theoretical support for demonstrating the efficiency of
                 context aware operators in transmitting schema
                 instances over the generations is introduced. The
                 suggested algorithm is experimentally validated in the
                 framework of a complex, industrial, nonlinear subsystem
                 of a sugar factory.",
  notes =        "Also known as \cite{6084927}",

Genetic Programming entries for Alina Patelli Lavinia Ferariu