Minyang Chen

Southern University of Science and Technology

Papers

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About

Minyang Chen is an emerging researcher specializing in evolutionary computation and black-box optimization, with a particular focus on advancing Differential Evolution (DE) algorithms through meta-learning and automated algorithm design. His most notable contribution to date is MetaDE, a pioneering framework that applies Differential Evolution to evolve and optimize DE itself — a sophisticated meta-evolutionary approach that addresses one of the field's most persistent challenges: hyperparameter sensitivity. This self-referential methodology represents a significant conceptual leap, enabling DE algorithms to autonomously adapt their configurations for peak performance across diverse and complex optimization landscapes without relying on manual tuning. The work, published in 2025, has already accumulated citations across multiple venues, reflecting its immediate resonance within the evolutionary computation community. Chen's research sits at the intersection of algorithm design automation and metaheuristic optimization, areas of growing importance as practitioners demand more robust, self-configuring solvers for real-world black-box problems. As a young researcher, his early contributions signal a promising trajectory in the development of intelligent, self-improving optimization frameworks with broad applicability across engineering and applied science domains.