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Gai-Ge Wang
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Gai-Ge Wang
Ocean University of China, China |
Biography:
Gai-Ge Wang obtained his master degree was in the field of intelligent planning and planning recognition at Northeast Normal University, Changchun, China. In 2010 he began working on his Ph.D for computational intelligence and its applications at Changchun Institute of Optics, Fine Mechanics and Physics, Chinese Academy of Sciences, Changchun, China. He is currently an associate professor in Ocean University of China, China. He is a visiting scholar in University of Alberta, Canada guided by Prof. Witold Pedrycz from November 2015. He has published 62 papers in total, including 47 journal papers, 4 book chapter/section, and 11 conference papers. 55 papers are indexed by SCI/EI (40 indexed by SCI, 19 indexed by EI). Furthermore, he is referee of other 50 international journals (Elsevier, IEEE, and Springer). He serves as a Guest Editor of Memetic Computing (Springer). His entire publications have been cited over 900 times (Google Scholar). Four of his papers have been cited over 50 times. One of his papers is selected as Highly Cited Paper by Web of Science, and eight of his papers is selected as Highly Cited Paper by Scopus (till Jan 2016). One of his papers is selected as "Top Articles from Outstanding S&T Journals of China-F5000 Frontrunner". The latest Google h-index and i10-index of his publications are 21 and 31, respectively. Also, his current RG Score is 28.18.
He is a senior member of SAISE and SCIEI, a member of IEEE, IEEE Computational Intelligence Society, ISMOST, Publications Chairs of ISCMI 2015, Advisory Committee of I3CS 2016, Technical Committee Chair of ICBDA 2016, and International Program Committee Member in twenty-five conferences. He is a member of the ISMOST, Publications Chairs of ISCMI 2015, and International Program Committee Member in more than 30 conferences. His research interests are swarm intelligence, soft computing, evolutionary computation, metaheuristic optimization and its applications in engineering, such as scheduling, path planning. He just proposed three bio-inspired metaheuristic algorithms, which are MBO (monarch butterfly optimization), EWA (earthworm optimization algorithm), EHO (elephant herding optimization).
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