Yu, T; Clack, C; (1997) PolyGP: A Polymorphic Genetic Programming System in Haskell. In: Koza, JR, (ed.) Late Breaking Papers at the 1997 Genetic Programming Conference. (pp. pp. 264-272). Stanford University Bookstore: Stanford, CA, USA.

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Abstract

In general, the machine learning process can be accelerated through the use of heuristic knowledge about the problem solution. For example, monomorphic typed Genetic Programming (GP) uses type information to reduce the search space and improve performance. Unfortunately, monomorphic typed GP also loses the generality of untyped GP: the generated programs are only suitable for inputs with the specified type. Polymorphic typed GP improves over monomorphic and untyped GP by allowing the type information to be expressed in a more generic manner, and yet still imposes constraints on the search space. This paper describes a polymorphic GP system which can generate polymorphic programs: programs which take inputs of more than one type and produces outputs of more than one type. We also demonstrate its operation through the generation of the “map” polymorphic program.

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