Abstract
In the era of Deep Learning and Big Data, the place of Genetic Programming (GP) within the Machine Learning area seems difficult to define. Whether it is due to technical constraints or conceptual barriers, GP is currently not a paradigm of choice for the development of state-of-the-art machine learning systems. Nonetheless, there are important features of the GP approach that make it unique and should continue to be actively explored and studied. In this work we focus on two aspects of GP that have previously received little or no attention, particularly in tree-based GP for symbolic regression. First, on the potential of GP to perform transfer learning, where solutions evolved for one problem are transferred to another. Second, on the potential of GP individuals to detect the true underlying structure of an input dataset and detect anomalies in the input data, what are known as outliers. This work presents initial results on both issues, with the goal of fostering discussion and showing that there is still untapped potential in the GP paradigm.
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Notes
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Experimental evidence more or less confirmed the No-Free-Lunch theorem in many domains where, on average, many algorithms tended to perform similarly.
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Acknowledgements
This research was funded by CONACYT (Mexico) Fronteras de la Ciencia 2015-2 Project No. FC-2015-2/944 and TecNM project no. 6826-18-P, and first and third authors were respectively supported by CONACYT graduate scholarship No. 302526 and No. 573397.
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Trujillo, L., Muñoz, L., López, U., Hernández, D.E. (2019). Untapped Potential of Genetic Programming: Transfer Learning and Outlier Removal. In: Banzhaf, W., Spector, L., Sheneman, L. (eds) Genetic Programming Theory and Practice XVI. Genetic and Evolutionary Computation. Springer, Cham. https://doi.org/10.1007/978-3-030-04735-1_10
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