Continuously Evolving Programs in Genetic Programming Using Gradient Descent

Created by W.Langdon from gp-bibliography.bib Revision:1.4524

  author =       "Will Smart and Mengjie Zhang",
  title =        "Continuously Evolving Programs in Genetic Programming
                 Using Gradient Descent",
  institution =  "Computer Science, Victoria University of Wellington",
  year =         "2004",
  number =       "CS-TR-04-10",
  address =      "New Zealand",
  keywords =     "genetic algorithms, genetic programming",
  URL =          "",
  URL =          "",
  abstract =     "gradient descent search in genetic programming for
                 continuously evolving genetic programs for object
                 classification problems. An inclusion factor is
                 introduced to each node except the root node in a
                 genetic program and gradient descent search is applied
                 to the inclusion factors. Three new on-zero operators
                 and two new continuous genetic operators are developed
                 for evolution. This approach is examined and compared
                 with a basic GP approach on three object classification
                 problems of varying difficulty. The results suggest
                 that the new approach can evolve genetic programs
                 continuously. The new method which uses the standard
                 genetic operators and gradient descent search applied
                 to the inclusion factors substantially outperforms the
                 basic GP approach which uses the standard genetic
                 operators but does not use the gradient descent and
                 inclusion factors. However, the new method with the
                 continuous operators and the gradient descent on
                 inclusion factors decreases the performance on all the

Genetic Programming entries for Will Smart Mengjie Zhang