MT-CGP: Mixed type cartesian genetic programming

Simon Harding, Vincent Graziano, Jürgen Leitner, Jürgen Schmidhuber

Research output: Chapter in Book/Report/Conference proceedingConference contribution

24 Scopus citations

Abstract

The majority of genetic programming implementations build expressions that only use a single data type. This is in contrast to human engineered programs that typically make use of multiple data types, as this provides the ability to express solutions in a more natural fashion. In this paper, we present a version of Cartesian Genetic Programming that handles multiple data types. We demonstrate that this allows evolution to quickly find competitive, compact, and human readable solutions on multiple classification tasks. © 2012 ACM.
Original languageEnglish (US)
Title of host publicationGECCO'12 - Proceedings of the 14th International Conference on Genetic and Evolutionary Computation
Pages751-758
Number of pages8
DOIs
StatePublished - Aug 13 2012
Externally publishedYes

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