- author = "Ibrahim Kuscu",
- title = "Evolutionary Generalisation and Genetic Programming",
- school = "School of Cognitive and Computing Sciences, University of Sussex",
- year = "1999",
- type = "DPhil Thesis",
- address = "UK",
- month = "13 " # jul,
- keywords = "genetic algorithms, genetic programming, Machine learning, Artificial intelligence Computer software Bionics",
-
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",
