Abstract
The tree-based representation model of Genetic Programming (GP) is largely used with subtree crossover for genetic variation. Unlike Cartesian Genetic Programming (CGP) which is commonly used merely with mutation. Compared to comprehensive knowledge about recombination in the field of tree-based GP, the state of knowledge in CGP appears to be comparatively poor. Even if CGP was officially introduced over twenty years ago, the role of recombination in CGP has been recently considered an open issue. Several promising steps have been taken in recent years, but more research is needed to develop towards a more comprehensive and holistic perspective on crossover in CGP. In this work, we propose a phenotypic variation method for discrete recombination in CGP. We compare our method to the traditional mutation-only CGP approach on a set of well-known symbolic regression problems. The initial results presented in this work demonstrate that the use of our proposed discrete recombination method performs significantly better than the traditional mutation-only approach.
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Kalkreuth, R. (2022). Towards Discrete Phenotypic Recombination in Cartesian Genetic Programming. In: Rudolph, G., Kononova, A.V., Aguirre, H., Kerschke, P., Ochoa, G., Tušar, T. (eds) Parallel Problem Solving from Nature – PPSN XVII. PPSN 2022. Lecture Notes in Computer Science, vol 13399. Springer, Cham. https://doi.org/10.1007/978-3-031-14721-0_5
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