Implement tree evaluator with linear scaling to improve convergence in symbolic regression.

[gkronber]

As described in "Improving Symbolic Regression with Interval Arithmetic and Linear Scaling", Keijzer, Proceedings of EuroGP'2003, LNCS 2610, pp 70-82, Springer Verlag, 2003.

[gkronber]

Added operators with r2579.

[gkronber]

The linear scaling operator generates different estimations for the same sample when a different range of rows is evaluated. This causes major headaches concerning model-analysis and validation.

Linear scaling should only be used in the learning process on the training and validation sets. The final model should contain the scaling factor and offset in the model structure so that it can be evaluated with a default tree evaluator (HL3TreeEvaluator).

[gkronber]

Added target variable property for the scaling tree evaluator with r2634.

[gkronber]

Removed the restriction of the estimated value to the range [LowerBound..UpperBound] because the estimation is scaled anyway. However the scaled values are restricted to that range with r2640.

[gkronber]

A linear scaling predictor builder is also needed to generate a correctly scaled final solution.

[gkronber]

Added a special predictor builder for linear scaling tree evaluation and changed default GP engines to use linear scaling evaluators with r2722.

[gkronber]

Moved linear scaling functionality out of tree evaluator into a separate operator with r2977.

[gkronber]

Fixed bug in regression engines with r2979.

[swagner]

Milestone Iteration 4 deleted

[swagner]

Milestone Current deleted