MT-CGP: mixed type cartesian genetic programming

Simon Harding, Vincent Graziano, Jürgen Leitner, Jürgen Schmidhuber

Research output: Chapter in Book/Report/Conference proceedingConference PaperResearchpeer-review

30 Citations (Scopus)

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.

Original languageEnglish
Title of host publicationGECCO'12 - Proceedings of the 14th International Conference on Genetic and Evolutionary Computation
PublisherIEEE, Institute of Electrical and Electronics Engineers
Pages751-758
Number of pages8
ISBN (Print)9781450311779
DOIs
Publication statusPublished - 2012
Externally publishedYes
EventThe Genetic and Evolutionary Computation Conference 2012 - Philadelphia, United States of America
Duration: 7 Jul 201211 Jul 2012
Conference number: 14th
https://dl.acm.org/doi/proceedings/10.1145/2330784 (Proceedings)

Conference

ConferenceThe Genetic and Evolutionary Computation Conference 2012
Abbreviated titleGECCO 2012
Country/TerritoryUnited States of America
CityPhiladelphia
Period7/07/1211/07/12
Internet address

Keywords

  • cartesian genetic programming
  • classifiers

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