Created by W.Langdon from gp-bibliography.bib Revision:1.8444
This exploratory paper suggests that reformulating the problem as a GP-based symbolic regression can achieve the same goal. The latent representation, in this case, is obtained as a byproduct of the solution to the problem of finding a parametric equation that represents a model of a family of signals (functions) that share the same equation, differing only for the values of a set of free parameters that appear in their definition.
This hypothesis is supported by a simple proof of concept based on the results of symbolic regression of a set of Gaussian functions. A discussion of possible issues that might need to be tackled when the method is applied to more complex real-world data and of the corresponding possible countermeasures concludes the paper.",
Proceedings published after the workshop in 2024 as 'Artificial Life and Evolutionary Computation'",
Genetic Programming entries for Giulia Magnani Monica Mordonini Stefano Cagnoni