Symbolic Regression Engine

This project focused on creating a symbolic regression engine capable of ingesting numerical data series and outputting candidate mathematical equations that explain the observed patterns.

By applying heuristic and evolutionary algorithms, the system automatically evolved potential models, pruning them based on error metrics and computational complexity.

This helped researchers and data scientists derive human-readable formulas without manually testing endless possibilities, greatly accelerating analytical workflows in fields such as finance, physics, and bioinformatics.