Incrementally learning the rules for supervised tasks:
the Monk's problems
Cognitive Science Research Paper, 396. School of Cognitive and Computing Sciences, University
of Sussex, Falmer, Brighton, Sussex, UK, (7 December 1995)Abstract
In previous experiments 45 evolution of variable
length mathematical expressions containing input
variables was found to be useful in finding learning
rules for simple and hard supervised tasks. However,
hard learning problems required special attention in
terms of their need for larger size codings of the
potential solutions and their ability of generalisation
over the testing set. This paper describes new
experiments aiming to find better solutions to these
issues. Rather than evolution a hill climbing strategy
with an incremental coding of potential solutions is
used in discovering learning rules for the three Monks'
problems. It is found that with this strategy larger
solutions can easily be coded for. Although a better
performance is achieved in training for the hard
learning problems, the ability of the generalisation
over the testing cases is observed to be poor.