A Rule-Based Approach for Constructing Neural Networks Using Genetic Programming

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

  author =       "Bret Talko",
  title =        "A Rule-Based Approach for Constructing Neural Networks
                 Using Genetic Programming",
  school =       "University of Melbourne",
  year =         "1999",
  month =        mar,
  keywords =     "genetic algorithms, genetic programming",
  broken =       "msc_thesis.ps.gz",
  URL =          "http://citeseer.ist.psu.edu/312140.html",
  size =         "166 pages",
  abstract =     "This thesis presents a novel use of Genetic
                 Programming (GP) to evolve recurrent, weightless neural
                 networks. The approach taken uses neural network
                 construction rules as the data structures that undergo
                 adaptation by the GP algorithm. These rules can be used
                 to construct a neural network by adding neurons and
                 connections to an initial basic network configuration.
                 In addition to evolving the architectures of networks,
                 the system evolves the formulae for the activation
                 function of each neuron in the networks and the number
                 of processing cycles for the networks.

                 The system has been applied to a number of Boolean
                 functions and it is shown that solution networks were
                 able to be found for each. Some variations in the
                 system design were investigated on the Boolean
                 functions to identify possible improvements that could
                 be made to the system which would result in better
                 performance. One variation to the system design which
                 resulted in a significantly large increase in the
                 system performance was made by changing the
                 construction rules that are used by the system.

                 A number of characteristics of the produced networks
                 were noted. Among them is the generation of network
                 construction rules that are similar to each other. A
                 system variation was made which succeeded in making the
                 rules more diverse but does not generally result in
                 better performance. Another characteristics of the
                 networks is that their construction rules often contain
                 unused and redundant rules.

                 The construction rules were designed to allow efficient
                 specification of networks which contain multiple
                 instances of the same sub-network. The system uses this
                 when discovering solution networks for Boolean
                 functions which can be decomposed into two identical
                 Boolean functions. Importantly, the system achieved
                 significantly better results than a modified version of
                 the system in which the features enabling efficient
                 network specification were not present. This suggests
                 that incorporating a modular construction process for
                 building networks is useful for obtaining solution
                 networks to decomposable problems.",
  notes =        "p129 XOR {"}Therefore Gruau's result is significantly
                 better than the GPNN result{"}",

Genetic Programming entries for Bret Talko