Modifying genetic programming for artificial neural network development for data mining

Created by W.Langdon from gp-bibliography.bib Revision:1.4420

  title =        "Modifying genetic programming for artificial neural
                 network development for data mining",
  author =       "Daniel Rivero and Julian Dorado and 
                 Juan R. Rabunal and Alejandro Pazos",
  journal =      "Soft Computing - A Fusion of Foundations,
                 Methodologies and Applications",
  year =         "2009",
  number =       "3",
  volume =       "13",
  pages =        "291--305",
  month =        feb,
  keywords =     "genetic algorithms, genetic programming, Artificial
                 neural networks, Evolutionary computation, Data mining,
                 Soft computing",
  DOI =          "doi:10.1007/s00500-008-0317-9",
  bibdate =      "2008-12-01",
  bibsource =    "DBLP,
  abstract =     "The development of artificial neural networks (ANNs)
                 is usually a slow process in which the human expert has
                 to test several architectures until he finds the one
                 that achieves best results to solve a certain problem.
                 However, there are some tools that provide the ability
                 of automatically developing ANNs, many of them using
                 evolutionary computation (EC) tools. One of the main
                 problems of these techniques is that ANNs have a very
                 complex structure, which makes them very difficult to
                 be represented and developed by these tools. This work
                 presents a new technique that modifies genetic
                 programming (GP) so as to correctly and efficiently
                 work with graph structures in order to develop ANNs.
                 This technique also allows the obtaining of simplified
                 networks that solve the problem with a small group of
                 neurons. In order to measure the performance of the
                 system and to compare the results with other ANN
                 development methods by means of evolutionary
                 computation (EC) techniques, several tests were
                 performed with problems based on some of the most used
                 test databases in the Data Mining domain. These
                 comparisons show that the system achieves good results
                 that are not only comparable to those of the already
                 existing techniques but, in most cases, improve them.",

Genetic Programming entries for Daniel Rivero Cebrian Julian Dorado Juan Ramon Rabunal Dopico Alejandro Pazos Sierra