HEROS (Heuristic Evolutionary Rule Optimization System) is an evolutionary rule-based machine learning (ERBML) algorithm framework for supervised learning. This scikit-learn compatible machine learning modeling package is designed to agnostically model simple/complex and/or clean/noisy problems (without hyperparameter optimization) and yield maximally human interpretable models. HEROS adopts a two-phase approach separating rule optimization, and rule-set (i.e. model) optimization, each with distinct multi-objective Pareto-front-based optimization. Rules are optimized based on maximizing rule-accuracy and instance coverage using a Pareto-inspired rule fitness function. Differently, models are optimized based on maximizing balanced accuracy and minimizing rule-set size using an NSGA-II-inspired evolutionary algorithm.
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