nsga-ii

TestNav is a Pareto-guided framework for compositional robustness testing in deep learning models, optimizing for both performance degradation and input fidelity to identify severe yet realistic failures.

Introduces RL-NSGA-II-GRC, a method integrating reinforcement learning with the NSGA-II genetic algorithm enhanced by gray relational coefficients for multi-objective optimization, applied to NASDAQ portfolio optimization. Achieves improved convergence and diversified Pareto fronts.