Bifurcation-Based Learning Algorithm (BBLA): A Novel Approach to Model Optimization via Iterative Divergence

This paper introduces the Bifurcation-Based Learning Algorithm (BBLA), a novel approach to model optimization that simulates iterative learning divergence through structured bifurcation. Inspired by decision theory and complex systems, BBLA incrementally branches a base model into sub-models by applying assumption-driven hyperparameter variations. Each iteration evaluates accuracy, performance, and drift through a composite reward function. The algorithm is validated on benchmark datasets and compared against supervised and unsupervised methods, showcasing competitive accuracy and enhanced robustness. BBLA offers a unique method for structured model discovery, adaptable to dynamic learning environments and applicable to both conventional ML workflows and emerging AI orchestration challenges.