GMOEA focuses on efficient offspring generation via learning from the distribution of promising solutions. GMOEA is capable of handling MOPs with up to 200 decision variables effectively, which is a new research direction for model-based evolutionary computation.

TREE is a large-scale multiobjective optimization test suite extracted from the power delivery system, aiming at handling real-time ratio error estimation of voltage transformers. Generally, the maximum number of decision variables is up to half a million, and it includes constraints, which guide the design of a meaningful evolutionary algorithm.

CSEA is a classification-based surrogate-assisted evolutionary algorithm, which uses an uncertainty configuration to balance between convergence and uncertainty. As an early attempt to use the classification model for capturing the dominance relationship in a reduced fitness landscape, which lies the foundation for future research on expensive high-dimensional optimization.