  The typical methods for symbolic regression produce rather abrupt changes in
solution candidates. In this work, we have tried to transform symbolic
regression from an optimization problem, with a landscape that is so rugged
that typical analysis methods do not produce meaningful results, to one that
can be compared to typical and very smooth real-valued problems. While the
ruggedness might not interfere with the performance of optimization, it
restricts the possibilities of analysis. Here, we have explored different
aspects of a transformation and propose a simple procedure to create
real-valued optimization problems from symbolic regression problems.
