Genetic programming: on the programming of computers by means of natural selection
Genetic programming: on the programming of computers by means of natural selection
Genetic programming: an introduction: on the automatic evolution of computer programs and its applications
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
Proceedings of the 8th annual conference on Genetic and evolutionary computation
GECCO '96 Proceedings of the 1st annual conference on Genetic and evolutionary computation
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In earlier papers we presented a technique ("RelaxGP") forimproving the performance of the solutions generated by Genetic Programming(GP) applied to regression and approximation of symbolicfunctions. RelaxGP changes the definition of a perfect solution: in standardsymbolic regression, a perfect solution provides exact values for eachpoint in the training set. RelaxGP allows a perfect solution to belong toa certain interval around the desired values. We applied RelaxGP to regression problems where the input data isnoisy. This is indeed the case in several "real-world" problems, wherethe noise comes, for example, from the imperfection of sensors. We comparethe performance of solutions generated by GP and by RelaxGP inthe regression of 5 noisy sets. We show that RelaxGP with relaxationvalues of 10% to 100% of the gaussian noise found in the data can outperform standard GP, both in terms of generalization error reached andin resources required to reach a given test error.