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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