Jin‐Kao Hao

Papers

Total Citations

H-Index

About

Jin‐Kao Hao is a leading figure in combinatorial optimization and evolutionary computation, renowned for developing high-performance algorithms that solve complex real-world problems. His research spans memetic algorithms, heuristic search, and artificial evolution, with a particular focus on tackling NP-hard challenges like the traveling salesman problem and its variants. Hao’s most-cited work, “An effective memetic algorithm for the close-enough traveling salesman problem” (2024, 14 citations), exemplifies his ability to blend local search with population-based methods, achieving state-of-the-art results on benchmark instances. His contributions have advanced the theory and practice of metaheuristics, influencing fields from logistics to bioinformatics. Hao’s edited volume “Artificial Evolution” (2012, 3 citations) further underscores his role in shaping the community through curated research. With a career marked by over 200 publications and thousands of citations, his work is a cornerstone for students and researchers seeking efficient, scalable solutions to optimization problems. Hao’s algorithms are not just academic—they drive practical innovations in scheduling, routing, and resource allocation.