{"ID":5443884,"CreatedAt":"2026-07-01T02:07:11.383974684Z","UpdatedAt":"2026-07-03T17:12:03.69683831Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2606.31990","arxiv_id":"2606.31990","title":"Evaluation of Population Initialization Methods for Genetic Programming-based Symbolic Regression","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.","short_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 (E...","url_abs":"https://arxiv.org/abs/2606.31990","url_pdf":"https://arxiv.org/pdf/2606.31990v1","authors":"[\"Lukas Kammerer\",\"Gabriel Kronberger\",\"Deaglan J. Bartlett\",\"Harry Desmond\",\"Pedro G. Ferreira\",\"Stephan Winkler\"]","published":"2026-06-30T17:28:37Z","proceeding":"cs.NE","tasks":"[\"cs.NE\",\"cs.LG\"]","methods":"[]","has_code":false}