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Plastic Fitness Predictors Coevolved with Cartesian Programs

  • Michal WiglaszEmail author
  • Michaela Drahosova
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9594)

Abstract

Coevolution of fitness predictors, which are a small sample of all training data for a particular task, was successfully used to reduce the computational cost of the design performed by cartesian genetic programming. However, it is necessary to specify the most advantageous number of fitness cases in predictors, which differs from task to task. This paper introduces a new type of directly encoded fitness predictors inspired by the principles of phenotypic plasticity. The size of the coevolved fitness predictor is adapted in response to the learning phase that the program evolution goes through. It is shown in 5 symbolic regression tasks that the proposed algorithm is able to adapt the number of fitness cases in predictors in response to the solved task and the program evolution flow.

Keywords

Fitness predictors Cartesian genetic programming Coevolution Phenotypic plasticity 

Notes

Acknowledgements

This work was supported by the Czech Science Foundation project 14-04197S. The authors thank the IT4Innovations Centre of Excellence for enabling these experiments.

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Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  1. 1.Faculty of Information TechnologyBrno University of TechnologyBrnoCzech Republic

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