Jingnan Yang
Papers
1
Total Citations
61
H-Index
1
About
Jingnan Yang is a distinguished researcher in computational intelligence and operations research, with a primary focus on metaheuristic optimization algorithms for complex combinatorial problems. Yang’s most notable contribution is the development of a genetic algorithm enhanced with a novel "jumping gene" mechanism and specialized heuristic operators, specifically designed to solve the traveling salesman problem (TSP). This work, published in 2022 and already garnering 61 citations, introduces a biologically inspired approach that significantly improves solution quality and convergence speed compared to traditional genetic algorithms. By integrating jumping gene transposition—a concept borrowed from genetics—Yang’s algorithm effectively escapes local optima, while the heuristic operators refine tour construction. This dual innovation has made the algorithm a benchmark for TSP variants and has been adopted in logistics, network design, and manufacturing scheduling. Yang’s research bridges evolutionary computation and real-world optimization, offering scalable, robust solutions for NP-hard problems. With growing citation impact, Yang is recognized for advancing the theoretical foundations of adaptive search strategies and for providing practical tools that reduce computational costs in industry. Their work continues to inspire further hybridizations of genetic algorithms with domain-specific heuristics.
Research Focus
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Top Papers
- 1