Abstract
Grammatical Evolution (GE) is a variant of Genetic Programming (GP) that uses a BNF-grammar to create syntactically correct solutions. GE is composed of the following components: the Problem Instance, the BNF-grammar (BNF), the Search Engine (SE) and the Mapping Process (MP). GE allows creating a distinction between the solution and search spaces using an MP and the BNF to translate from genotype to phenotype, that avoids invalid solutions that can be obtained with GP. One genotype can generate different phenotypes using a different MP. There exist at least three MPs widely used in the art-state: Depth-first (DF), Breadth-first (BF) and \( \pi \)Grammatical Evolution (piGE). In the present work DF, BF, and piGE have been studied in the Symbolic Regression Problem. The results were compared using a statistical test to determine which MP gives the best results.
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Acknowledgements
The authors want to thank National Council for Science and Technology of Mexico (CONACyT) through the scholarship for postgraduate studies: 703582 (B. Zuñiga) and the Research Grant CÁTEDRAS-2598 (A. Rojas), the Leín Institute of Technology (ITL), and the Guanajuato University for the support provided for this research.
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Zuñiga-Nuñez, B.V. et al. (2020). Studying Grammatical Evolution’s Mapping Processes for Symbolic Regression Problems. In: Castillo, O., Melin, P., Kacprzyk, J. (eds) Intuitionistic and Type-2 Fuzzy Logic Enhancements in Neural and Optimization Algorithms: Theory and Applications. Studies in Computational Intelligence, vol 862. Springer, Cham. https://doi.org/10.1007/978-3-030-35445-9_32
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