Abstract
In earlier papers we presented a technique (“RelaxGP”) for improving the performance of the solutions generated by Genetic Programming (GP) applied to regression and approximation of symbolic functions. RelaxGP changes the definition of a perfect solution: in standard symbolic regression, a perfect solution provides exact values for each point in the training set. RelaxGP allows a perfect solution to belong to a certain interval around the desired values.
We applied RelaxGP to regression problems where the input data is noisy. This is indeed the case in several “real-world” problems, where the noise comes, for example, from the imperfection of sensors. We compare the performance of solutions generated by GP and by RelaxGP in the regression of 5 noisy sets. We show that RelaxGP with relaxation values of 10% to 100% of the gaussian noise found in the data can outperform standard GP, both in terms of generalization error reached and in resources required to reach a given test error.
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Da Costa, L., Landry, JA., Levasseur, Y. (2008). Treating Noisy Data Sets with Relaxed Genetic Programming. In: Monmarché, N., Talbi, EG., Collet, P., Schoenauer, M., Lutton, E. (eds) Artificial Evolution. EA 2007. Lecture Notes in Computer Science, vol 4926. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-79305-2_1
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DOI: https://doi.org/10.1007/978-3-540-79305-2_1
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