An adaptive knowledge-acquisition system using generic genetic programming

Created by W.Langdon from gp-bibliography.bib Revision:1.4221

  author =       "Man Leung Wong",
  title =        "An adaptive knowledge-acquisition system using generic
                 genetic programming",
  journal =      "Expert Systems with Applications",
  volume =       "15",
  pages =        "47--58",
  year =         "1998",
  number =       "1",
  keywords =     "genetic algorithms, genetic programming",
  ISSN =         "0957-4174",
  DOI =          "doi:10.1016/S0957-4174(98)00010-4",
  URL =          "",
  URL =          "",
  size =         "12 pages",
  abstract =     "The knowledge-acquisition bottleneck greatly obstructs
                 the development of knowledge-based systems. One popular
                 approach to knowledge acquisition uses inductive
                 concept learning to derive knowledge from examples
                 stored in databases. However, existing learning systems
                 cannot improve themselves automatically. This paper
                 describes an adaptive knowledge-acquisition system that
                 can learn first-order logical relations and improve
                 itself automatically. The system is composed of an
                 external interface, a biases base, a knowledge base of
                 background knowledge, an example database, an empirical
                 ILP learner, a meta-level learner, and a learning
                 controller. In this system, the empirical ILP learner
                 performs top-down search in the hypothesis space
                 defined by the concept description language, the
                 language bias, and the background knowledge. The search
                 is directed by search biases which can be induced and
                 refined by the meta-level learner based on generic
                 genetic programming.

                 It has been demonstrated that the adaptive
                 knowledge-acquisition system performs better than FOIL
                 on inducing logical relations from perfect or noisy
                 training examples. The result implies that the search
                 bias evolved by evolutionary learning is better than
                 that of FOIL which is designed by a top researcher in
                 the field. Consequently, generic genetic programming is
                 a promising technique for implementing a meta-level
                 learning system. The result is very encouraging as it
                 suggests that the process of natural selection and
                 evolution can successfully evolve a high-performance
                 learning system.",

Genetic Programming entries for Man Leung Wong