Coevolution in cartesian genetic programming

Authors:
Michaela Šikulová;Lukáš Sekanina
Affiliations:
Faculty of Information Technology, IT4Innovations Centre of Excellence, Brno University of Technology, Brno, Czech Republic;Faculty of Information Technology, IT4Innovations Centre of Excellence, Brno University of Technology, Brno, Czech Republic
Venue:
EuroGP'12 Proceedings of the 15th European conference on Genetic Programming
Year:
2012

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Abstract

Cartesian genetic programming (CGP) is a branch of genetic programming which has been utilized in various applications. This paper proposes to introduce coevolution to CGP in order to accelerate the task of symbolic regression. In particular, fitness predictors which are small subsets of the training set are coevolved with CGP programs. It is shown using five symbolic regression problems that the (median) execution time can be reduced 2---5 times in comparison with the standard CGP.