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 language||English (US)|
|Title of host publication||GECCO'12 - Proceedings of the 14th International Conference on Genetic and Evolutionary Computation|
|Number of pages||8|
|State||Published - Aug 13 2012|