📝 Symbolic Regression Tutorial: Add clarity on eval function values

doc/examples/gp_symbreg.rst

@@ -18,7 +18,15 @@ individual's fitness.
 In this example, we use a classical distribution, the quartic polynomial
 :math:`(x^4 + x^3 + x^2 + x)`, a one-dimension distribution. *20* equidistant
 points are generated in the range [-1, 1], and are used to evaluate the
-fitness.
+fitness. 
+
+Note that in this example, the ``evalSymbReg`` function has the polynomial being 
+regressed to hard coded, and thus the :math:`y` value can be calculated for each
+value of :math:`x`. If one were using the system to fit other data, the hard coded
+quadratic polynomial expression would need to be replaced. For example,
+:math:`\frac{1}{n}\sum_{i=1}^{n}(\hat{y_{i}} - y_{i})^2`, where :math:`\hat{y_{i}}` is 
+a candidate solution's predicted value based on the independent variables, and 
+:math:`y_{i}` is the the expected value. 
 
 
 Creating the primitives set