Genetic programming using a minimum description length principle
Advances in genetic programming
Improving symbolic regression with interval arithmetic and linear scaling
EuroGP'03 Proceedings of the 6th European conference on Genetic programming
No free lunch theorems for optimization
IEEE Transactions on Evolutionary Computation
Proceedings of the 8th annual conference on Genetic and evolutionary computation
Non-intrusive quality evaluation of VoIP using genetic programming
Proceedings of the 1st international conference on Bio inspired models of network, information and computing systems
VoIP speech quality estimation in a mixed context with genetic programming
Proceedings of the 10th annual conference on Genetic and evolutionary computation
On Improving Generalisation in Genetic Programming
EuroGP '09 Proceedings of the 12th European Conference on Genetic Programming
Modeling Pheromone Dispensers Using Genetic Programming
EvoWorkshops '09 Proceedings of the EvoWorkshops 2009 on Applications of Evolutionary Computing: EvoCOMNET, EvoENVIRONMENT, EvoFIN, EvoGAMES, EvoHOT, EvoIASP, EvoINTERACTION, EvoMUSART, EvoNUM, EvoSTOC, EvoTRANSLOG
ARIMA models versus gene expression programming in precipitation modeling
EC'09 Proceedings of the 10th WSEAS international conference on evolutionary computing
Proceedings of the 11th Annual Conference Companion on Genetic and Evolutionary Computation Conference: Late Breaking Papers
Genetic programming for QSAR investigation of docking energy
Applied Soft Computing
Classification of oncologic data with genetic programming
Journal of Artificial Evolution and Applications - Special issue on artificial evolution methods in the biological and biomedical sciences
Genetic programming for anticancer therapeutic response prediction using the NCI-60 dataset
Computers and Operations Research
Real-time, non-intrusive evaluation of VoIP
EuroGP'07 Proceedings of the 10th European conference on Genetic programming
Real-time, non-intrusive speech quality estimation: a signal-based model
EuroGP'08 Proceedings of the 11th European conference on Genetic programming
On the importance of data balancing for symbolic regression
IEEE Transactions on Evolutionary Computation
The estimation of hölderian regularity using genetic programming
Proceedings of the 12th annual conference on Genetic and evolutionary computation
Knowledge mining with genetic programming methods for variable selection in flavor design
Proceedings of the 12th annual conference on Genetic and evolutionary computation
Constant versus variable arity operators in genetic programming
Proceedings of the 12th annual conference on Genetic and evolutionary computation
PID step response using genetic programming
SEAL'10 Proceedings of the 8th international conference on Simulated evolution and learning
Drawing boundaries: using individual evolved class boundaries for binary classification problems
Proceedings of the 13th annual conference on Genetic and evolutionary computation
Overfitting detection and adaptive covariant parsimony pressure for symbolic regression
Proceedings of the 13th annual conference companion on Genetic and evolutionary computation
Data mining using unguided symbolic regression on a blast furnace dataset
EvoApplications'11 Proceedings of the 2011 international conference on Applications of evolutionary computation - Volume Part I
Macro-economic time series modeling and interaction networks
EvoApplications'11 Proceedings of the 2011 international conference on Applications of evolutionary computation - Volume Part II
Genetic Programming and Evolvable Machines
Improving the parsimony of regression models for an enhanced genetic programming process
EUROCAST'11 Proceedings of the 13th international conference on Computer Aided Systems Theory - Volume Part I
Evolving estimators of the pointwise Hölder exponent with Genetic Programming
Information Sciences: an International Journal
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Performing a linear regression on the outputs of arbitrary symbolic expressions has empirically been found to provide great benefits. Here some basic theoretical results of linear regression are reviewed on their applicability for use in symbolic regression. It will be proven that the use of a scaled error measure, in which the error is calculated after scaling, is expected to perform better than its unscaled counterpart on all possible symbolic regression problems. As the method (i) does not introduce additional parameters to a symbolic regression run, (ii) is guaranteed to improve results on most symbolic regression problems (and is not worse on any other problem), and (iii) has a well-defined upper bound on the error, scaled squared error is an ideal candidate to become the standard error measure for practical applications of symbolic regression.