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Symbolic regression is a regression analysis where we search for the mathematical expression that best describes the relationship between our independent variables and our dependent variable. pySRURGS is a python code for symbolic regression by uniform random global search. Typically, symbolic regression problems are solved using genetic programming (Koza, 1994), but the data demonstrating that genetic programming outperforms random search on symbolic regression problems is lacking. Comparing the performance of an algorithm against that of random search demonstrates the advantage gained by the algorithm’s internal machinery. Our review of the literature found a large study examining the parameter space of genetic programming, which found few patterns with regards to how users should select genetic algorithm hyperparameters (Sipper, Fu, Ahuja, & Moore, 2018). These workers attempt to compare their genetic algoritm’s performance to that of random search on several problems, including a symbolic regression problem. We reproduce their work and find they unfairly disadvantaged the random search by forcing it to only attempt the simplest solutions. There is little evidence that genetic algorithms outperform random search on the symbolic regression problem and the creation of pySRURGS addresses this need.

pySRURGS - a python package for symbolic regression by uniform random global search