Evaluation of Population Initialization Methods for Genetic Programming-based Symbolic Regression
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...
| Authors | Lukas Kammerer, Gabriel Kronberger, Deaglan J. Bartlett et al. |
|---|---|
| Primary category | cs.NE |
| Published | 30 June 2026 |
| Last updated at source | 30 June 2026 |
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