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Indirectly Encoded Fitness Predictors Coevolved with Cartesian Programs

  • Michaela SikulovaEmail author
  • Jiri Hulva
  • Lukas Sekanina
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9025)

Abstract

We investigate coevolutionary Cartesian genetic programming that coevolves fitness predictors in order to diminish the number of target objective vector (TOV) evaluations, needed to obtain a satisfactory solution, to reduce the computational cost of evolution. This paper introduces the use of coevolution of fitness predictors in CGP with a new type of indirectly encoded predictors. Indirectly encoded predictors are operated using the CGP and provide a variable number of TOVs used for solution evaluation during the coevolution. It is shown in 5 symbolic regression problems that the proposed predictors are able to adapt the size of TOVs array in response to a particular training data set.

Keywords

Coevolution Cartesian genetic programming Fitness prediction 

Notes

Acknowledgments

This work was supported by the Czech science foundation project 14-04197S, the Brno University of Technology project FIT-S-14-2297 and the IT4Innovations Centre of Excellence CZ.1.05/1.1.00/02.0070.

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Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  1. 1.Faculty of Information Technology, IT4Innovations Centre of ExcellenceBrno University of TechnologyBrnoCzech Republic

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