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.
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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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Sikulova, M., Hulva, J., Sekanina, L. (2015). Indirectly Encoded Fitness Predictors Coevolved with Cartesian Programs. In: Machado, P., et al. Genetic Programming. EuroGP 2015. Lecture Notes in Computer Science(), vol 9025. Springer, Cham. https://doi.org/10.1007/978-3-319-16501-1_10
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DOI: https://doi.org/10.1007/978-3-319-16501-1_10
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