2008 | OriginalPaper | Chapter
Treating Noisy Data Sets with Relaxed Genetic Programming
Authors : Luis Da Costa, Jacques-André Landry, Yan Levasseur
Published in: Artificial Evolution
Publisher: Springer Berlin Heidelberg
Activate our intelligent search to find suitable subject content or patents.
Select sections of text to find matching patents with Artificial Intelligence. powered by
Select sections of text to find additional relevant content using AI-assisted search. powered by
In earlier papers we presented a technique (“
RelaxGP
”) for improving the performance of the solutions generated by
G
enetic
P
rogramming (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.