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Analyzing optimized constants in genetic programming on a real-world regression problem

Ephemeral random constants are commonly used for symbolic regression with genetic programming. However, due to their random nature, it is difficult for genetic programming to find proper constants. This often leads to large and complex non-interpretable solutions. Therefore, in this short paper, we analyze a method that replaces ephemeral random constants with constant tokens whose values are optimized during evolution with the Sequential Least Squares Programming method. The results achieved on the studied regression problem indicate that replacing ephemeral random constants by optimized constant tokens leads to solutions with a significantly lower error which are also smaller than the solutions generated by a standard GP approach using ephemeral random constants.