Coefficient Calibration as Selection Pressure in Symbolic Regression
Read the original on arXiv Machine Learning →The paper introduces Dirichlet‑Sinkhorn Constant Averaging (DSCA), a calibration method for memetic symbolic regression that partitions data into subsets with varying covariate distributions, calibrates each candidate independently, and evaluates it using the mean of the resulting parameters. DSCA reduces the excess loss compared to centralized calibration for correctly specified models and improves functional recovery and the accuracy‑complexity trade‑off on synthetic and real datasets. The authors demonstrate that DSCA’s performance gains stem from a controlled heterogeneous calibration that adds a complementary source of selection pressure in symbolic‑regression search.
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