Inproceedings,
Genetic Programming of Fuzzy Logic Production Rules
1995 IEEE Conference on Evolutionary Computation, 2, page 765. Perth, Australia, IEEE Press, (29 November - 1 December 1995)
Abstract
John Koza has demonstrated that a form of machine
learning can be constructed by using the techniques of
Genetic Programming using LISP statements. We describe
here an extension to this principle using Fuzzy Logic
sets and operations instead of LISP expressions. We
show that Genetic programming can be used to generate
trees of fuzzy logic statements, the evaluation of
which optimise some external process, in our example
financial trading. We also show that these trees can be
simply converted to natural language rules, and that
these rules are easily comprehended by a lay audience.
This clarity of internal function can be compared to
Black Box non-parametric modelling techniques such as
Neural Networks. We then show that even with minimal
data preparation the technique produces rules with good
out of sample performance on a range of different
financial instruments.
