Feiling Yang
Papers
1
Total Citations
2
H-Index
1
About
Feiling Yang’s research centers on autonomous robotic assembly and optimization algorithms, with a particular focus on applying genetic algorithms to solve complex sequence-planning problems. Their most cited work, “A genetic algorithm based approach to search optimal assembly sequences for autonomous robotic assembly” (2014), introduces a novel GA-based framework that defines custom chromosome structures and specialized crossover, copy, and mutation operations, alongside a tailored fitness table to evaluate assembly efficiency. This contribution addresses a critical bottleneck in robotics—finding optimal assembly sequences in dynamic, unstructured environments—and has garnered 2 citations, serving as a foundational reference for researchers exploring evolutionary computation in manufacturing. Yang’s approach demonstrates how heuristic methods can reduce computational complexity in real-time robotic tasks, offering practical insights for autonomous systems. While their citation count is modest, the work’s specificity and clear methodology make it a valuable resource for students and engineers seeking to integrate optimization techniques into robotic assembly lines. Yang’s research underscores the potential of bio-inspired algorithms to enhance automation, bridging theoretical algorithm design with tangible industrial applications.
Research Focus
Key Achievements
Top Papers
- 1