Abstract
Recent experiments with a genetic-based encoding
schema are presented as a potentially powerful tool to
discover learning rules by means of evolution. The
representation used is similar to the one used in
Genetic Programming (GP) but it employs only a fixed
set of functions to solve a variety of problems. In
this paper three Monks' and parity problems are tested.
The results indicate the usefulness of the encoding
schema in discovering learning rules for hard learning
problems. The problems and future research directions
are discussed within the context of GP practices.
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