Created by W.Langdon from gp-bibliography.bib Revision:1.5080
The aim of this paper is to investigate if a hybrid, tree-based GP implementation written for symbolic regression purposes can be improved in terms of reliability and precision of the results both by several modifications of the standard GP components and by pre-processing the input data set.
In order to increase variability, the effect of a simple archive updating strategy and of a periodical killing of a large part of the population (with the insertion of new and composed individuals) is assessed. As a promising measure to preserve variation among individuals, a MinMax approach in the definition of the fitness function is also proposed and tested as an alternative to the plain aggregating approach.
With regard to expressivity, a simple solution consisting in the definition of a unary function that introduces a translation in the argument of the function itself is put forward.
Other experiments are performed to assess if the redefinition of the fitness function using a normalised error can have beneficial effects on the evolution, as an alternative to the common root mean square error.
Finally, the splitting of the input data set in two different subsets, respectively for parameter tuning and fitness evaluation, is investigated.",
School of Civil Engineering, University of Leeds, LS2 9JT, UK",
Genetic Programming entries for Umberto Armani Vassili V Toropov Andrey Polynkin Osvaldo M Querin Luis F Alvarez