Symbolic Regression Problem

Problem Parameters:

Parameter	Description
BestKnownQuality	The minimal error value that reached by symbolic regression solutions for the problem.
DataAnalysisProblemData	The data set, target variable and input variables of the data analysis problem.
Evaluator	SymbolicRegressionScaledMeanSquaredErrorEvaluator: The operator which should be used to evaluate symbolic regression solutions.
FunctionTreeGrammar	The grammar that should be used for symbolic regression models.
LowerEstimationLimit	The lower limit for the estimated value that can be returned by the symbolic regression model.
MaxExpressionDepth	Maximal depth of the symbolic expression.
MaxExpressionLength	Maximal length of the symbolic expression.
MaxFunctionArguments	Maximal number of arguments of automatically defined functions.
MaxFunctionDefiningBranches	Maximal number of automatically defined functions.
Maximization	Set to false as the error of the regression model should be minimized.
SolutionCreator	ProbabilisticTreeCreator: The operator which should be used to create new symbolic regression solutions.
SymbolicExpressionTreeInterpreter	The interpreter that should be used to evaluate the symbolic expression tree.
UpperEstimationLimit	The upper limit for the estimated value that can be returned by the symbolic regression model.

Is there a sample/tutorial?

Sure. We have configured a standard genetic programming algorithm to solve a symbolic regression problem (Boston Housing dataset):

• Symbolic Regression + Genetic Programming