Genetic programming II: automatic discovery of reusable programs
Genetic programming II: automatic discovery of reusable programs
Genetic programming: an introduction: on the automatic evolution of computer programs and its applications
Machine Learning
Proceedings of the 6th International Conference on Parallel Problem Solving from Nature
PPSN VI Proceedings of the 6th International Conference on Parallel Problem Solving from Nature
Genetic Programming with Dynamic Fitness for a Remote Sensing Application
PPSN VI Proceedings of the 6th International Conference on Parallel Problem Solving from Nature
Long memory time series forecasting by using genetic programming
Genetic Programming and Evolvable Machines
Validation sets for evolutionary curtailment with improved generalisation
ICHIT'11 Proceedings of the 5th international conference on Convergence and hybrid information technology
Random sampling technique for overfitting control in genetic programming
EuroGP'12 Proceedings of the 15th European conference on Genetic Programming
Prediction of forest aboveground biomass: an exercise on avoiding overfitting
EvoApplications'13 Proceedings of the 16th European conference on Applications of Evolutionary Computation
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Overfitting the training data is a common problem in supervised machine learning. When dealing with a remote sensing inverse problem, the PAR, overfitting prevents GP evolved models to be successfully applied to real data. We propose to use a classic method of overfitting control by the way of a validation set. This allows to go backward in the evolution process in order to retrieve previous, not yet overfitted models. Although this "backwarding" method performs well on academic benchmarks, there is not enough improvement to deal with the PAR. A new backwarding criterion is then derived using real satellite data and the knowledge of plausible physical bounds for the PAR coefficient in the geographical area that is monitored. This leads to satisfactory GP models and drastically improved images.