Evolutionary Generalisation and Genetic Programming

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

  author =       "Ibrahim Kuscu",
  title =        "Evolutionary Generalisation and Genetic Programming",
  school =       "School of Cognitive and Computing Sciences",
  year =         "1999",
  type =         "DPhil Thesis",
  address =      "University of Sussex",
  month =        "13 " # jul,
  keywords =     "genetic algorithms, genetic programming, Machine
                 learning, Artificial intelligence Computer software
  URL =          "http://www.mgovernment.org/resurces/myphdthesis.pdf",
  URL =          "http://ethos.bl.uk/OrderDetails.do?did=11&uin=uk.bl.ethos.285062",
  size =         "178 pages",
  abstract =     "A closer examination of the learning research in
                 Artificial Intelligence reveals that the views of
                 learning as perceived by researchers using genetic
                 based methods seem to be different than the views
                 employed by traditional Machine Learning (ML) research.
                 Genetic based learning tends to favour a continuous
                 learning with self organising learning systems, which
                 is mainly performance or problem solving oriented. ML
                 research, on the other hand, tend to develop systems
                 with human crafted internal structures, which are
                 strong in reaching the goals set by the theoretical
                 work in the area. One of such goals, establishing
                 necessary theoretical grounds, aims at developing
                 learning systems which generalise what they learn to
                 new but relevant tasks.

                 The thesis aims at bridging the gap between traditional
                 and genetic based learning research by promoting
                 interaction between the two with respect to performance
                 expectations from a learner in the form of
                 generalisation. In searching for this beneficial
                 interaction, the thesis explores the tendency in
                 genetic based methods towards a memorisation (i.e.,
                 simple look-up table) and compression (i.e., a compact
                 re-representation) oriented learning and emphasises the
                 necessity and the requirements for generalisation
                 (i.e., predictive accuracy in responding to unseen
                 cases) oriented learning.

                 A particular emphasis is given to a sub-area of genetic
                 based learning research called genetic programming
                 (GP). After identifying the lack of proper
                 consideration of generalisation in GP, several
                 experiments involving both supervised learning problems
                 and simulations of learning behaviours are developed in
                 order to explore the ways in which the generalisation
                 performance of the solutions produced by GP can be
                 improved. The findings of these GP experiments reflect
                 that borrowing some of the principles from traditional
                 learning research provides significant ways of
                 improvement in the approaches to learning in the form
                 of evolutionary generalisation. One of the experiments
                 suggests that generalisation of learnt behaviours are
                 possible by using a training regime based on
                 environment sampling. Another set of experiments
                 suggest that generalisation in GP can be improved by
                 selection of a set of non-problem-specific functions.
                 Finally, other than improving on the standard
                 applications of GP, a set of experiments presents how
                 GP can be used in improving performance of other
                 learners such as back-propagation.

                 Out of many possible ways of a beneficial interaction
                 with the traditional learning methods, only a few could
                 be presented in this study. There is, however, an
                 inevitable necessity and rich potential for future
                 improvements in the area, which are also presented in
                 this thesis.",
  notes =        "\cite{kushchu:2002:AIR} gives the title as
                 {"}Evolutionary Generalisation and Genetic
                 Programming{"} uk.bl.ethos.285062",

Genetic Programming entries for Ibrahim Kuscu