Evaluation of Population Initialization Methods for Genetic Programming-based Symbolic Regression

• URL: http://arxiv.org/abs/2606.31990v1
• Date: Tue, 30 Jun 2026 17:28:37 GMT
• Title: Evaluation of Population Initialization Methods for Genetic Programming-based Symbolic Regression
• Abstract summary: We analyze the effect of optimizing the initial population of genetic programming (GP) for symbolic regression (SR) on the accuracy and complexity of solutions.<n>We compare twelve synthetic problems of varying complexity and one real-world dataset.
• Score: 0.36944296923226316
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: We analyze the effect of optimizing the initial population of genetic programming (GP) for symbolic regression (SR) on the accuracy and complexity of solutions. We compare three well-established random initialization methods as well as initialization with small optimized solutions from exhaustive symbolic regression (ESR) using a GP/SR implementation which is based on the multi-objective evolutionary algorithm NSGA-II. We compare the final Pareto fronts found with each initialization method on twelve synthetic problems of varying complexity and one real-world dataset. We find no significant differences in accuracy or model complexity among the initialization methods. The initial advantage of initialization with ESR disappears after only a few generations. Our results show that, given similar diversity in the initial population, the effect of the initialization method in GP-based symbolic regression on the final Pareto front is negligible.

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