C. Feng

Southern University of Science and Technology

Papers

Total Citations

H-Index

About

C. Feng is an emerging researcher specializing in **Evolutionary Computation (EC)**, with a particular focus on meta-optimization and algorithm design for black-box optimization problems. Their most notable contribution is **MetaDE**, a pioneering framework that applies Differential Evolution (DE) to evolve and optimize DE itself — a sophisticated meta-learning approach that addresses one of the field's most persistent challenges: hyperparameter sensitivity. By automating the tuning process that traditionally requires significant domain expertise, Feng's work represents a meaningful step toward self-configuring evolutionary algorithms capable of achieving peak performance without manual intervention. Published in 2025, MetaDE has already garnered **13 combined citations**, a promising early indicator of its impact within the evolutionary computation community. The work reflects a growing trend toward algorithm-level automation and meta-learning in optimization, positioning Feng at the intersection of two rapidly advancing fields. For students and researchers grappling with algorithm configuration in challenging optimization landscapes, Feng's contributions offer both theoretical insights and practical tools. Though early in their career, C. Feng demonstrates a sharp focus on solving fundamental bottlenecks in evolutionary strategies through elegant, self-referential computational thinking.