Plastic Fitness Predictors Coevolved with Cartesian Programs
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.
KeywordsFitness predictors Cartesian genetic programming Coevolution Phenotypic plasticity
This work was supported by the Czech Science Foundation project 14-04197S. The authors thank the IT4Innovations Centre of Excellence for enabling these experiments.
- 2.Ellefsen, K.O.: Balancing the costs and benefits of learning ability. In: Advances in Artificial Life, ECAL 2013, vol. 12, pp. 292–299. MIT Press (2013)Google Scholar
- 3.Ellefsen, K.O.: Evolved sensitive periods in learning. In: Advances in Artificial Life, ECAL 2013, vol. 12, pp. 409–416. MIT Press (2013)Google Scholar
- 5.Imamura, K., Foster, J.A., Krings, A.W.: The test vector problem and limitations to evolving digital circuits. In: Proceedings of the 2nd NASA/DoD Workshop on Evolvable Hardware, pp. 75–79. IEEE Computer Society (2000)Google Scholar
- 8.Popovici, E., Bucci, A., Wiegand, R.P., de Jong, E.D.: Coevolutionary principles. In: Rozenberg, G., Bäck, T., Kok, J.N. (eds.) Handbook of Natural Computing, pp. 988–1028. Springer, New York (2011)Google Scholar
- 10.Sikulova, M., Hulva, J., Sekanina, L.: Indirectly encoded fitness predictors coevolved with cartesian programs. In: Machado, P., Heywood, M.I., McDermott, J., Castelli, M., García-Sánchez, P., Burelli, P., Risi, S., Sim, K. (eds.) Genetic Programming. LNCS, vol. 9025. Springer, Heidelberg (2015)Google Scholar