@inproceedings{discovery10087119,
           title = {PolyGP: a polymorphic genetic programming system in Haskell},
            year = {1998},
       publisher = {Morgan Kaufman},
       booktitle = {Genetic Programming 1998: Proceedings of the Third Annual Conference},
         address = {San Francisco, CA},
           pages = {416--421},
            note = {This version is the author accepted manuscript. For information on re-use, please refer to the publisher's terms and conditions.},
        abstract = {In general, the machine learning
process can be accelerated through the
use of additional 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 produce outputs of more than one
type.},
             url = {https://discovery.ucl.ac.uk/id/eprint/10087119/},
          author = {Yu, T and Clack, C}
}