GenForge: A Multi-population Genetic Programming framework with Semantic-Preserving Feature Partitioning for classification and regression tasks

GenForge is an open-source Python package for interpretable symbolic modeling through multi-population genetic programming. It unifies regression, classification, and semantic feature partitioning into a single evolutionary learning framework. By integrating multi-gene symbolic regression, ensemble evolution, and Semantic-Preserving Feature Partitioning (SPFP), GenForge enables high-fidelity modeling while maintaining transparency and parsimony. The package provides modules for symbolic regression (gpregressor), classification (gpclassifier), and feature partitioning (SPFPPartitioner), each with reproducible example scripts and diagnostic visualization tools. GenForge supports reproducible research and educational use in explainable AI, symbolic learning, and multi-view ensemble modeling. • Multi-population GP with ensemble fusion for robust, parsimonious models. • Semantic-Preserving Feature Partitioning builds balanced, low-redundancy views. • Supports regression and classification with interpretable symbolic equations. • Interpretable symbolic models support auditability and scientific insight. • Reproducible reports, diagnostics, and APIs for research and education.