Smooth Symbolic Regression: Transformation of Symbolic Regression into a Real-valued Optimization Problem
This work addresses a methodological bottleneck for researchers in symbolic regression, though it appears incremental as it focuses on improving analysis rather than performance.
The authors tackled the problem of symbolic regression's rugged optimization landscape, which hinders analysis, by transforming it into a smooth real-valued optimization problem, proposing a simple procedure for this conversion.
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.