John Koza~7 has demonstrated that a form of machine learning can be constructed by using the techniques of Genetic Progamming using LISP statements. We describe here an extension to this principle using Fuzzy Logic sets and operations instead of LSIP 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 eadily 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 smaple performance on a range of diffeent financial instruments.
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