Details

Technologies for Next-Generation Network Environments.- Recent Progress in Computational Electromagnetic Dynamics.- Future Cyber Security, Privacy and Forensics for Advanced systems.- Data Mining Techniques and its Applications.- Optimization Models in Deep Learning/Machine Learning.

<p><b>Jeng-Shyang Pan </b>received the B. S. degree in Electronic Engineering from the National Taiwan University of Science and Technology in 1986, the M. S. degree in Communication Engineering from the National Chiao Tung University, Taiwan in 1988, and the Ph.D. degree in Electrical Engineering from the University of Edinburgh, U.K. in 1996. Currently He is working as the Professor at the School of Artificial Intelligence, Nanjing&nbsp;University of Information Science and Technology. He has published more than 800 papers in which more than 500 papers are indexed by SCI, the H-Index is 71&nbsp;and the total cited times are more than 20000. He is the IET Fellow, UK and has been the Vice Chair of IEEE Tainan Section. He was Awarded Gold Prize in the International Micro Mechanisms Contest held in Tokyo, Japan in 2010. He was also awarded Gold Medal in the Pittsburgh Invention & New Product Exposition (INPEX) in 2010, Gold Medal in the International Exhibition of Geneva Inventions in 2011&nbsp;and Gold Medal of the IENA, International “Ideas – Inventions – New products“, Nuremberg, Germany in 2011. His current research interests include computational intelligence, artificial intelligence and information hiding.</p><p><br></p><p><b>Jerry Chun-Wei Lin</b> is currently working as the full Professor at the Department of Computer Science,&nbsp;Electrical Engineering and Mathematical Sciences, Western Norway University of Applied Sciences,&nbsp;Bergen, Norway. He has published 500+ research papers in refereed journals and is the leader of the well-known SPMF project, which provides more than 190 data mining algorithms and has been widely cited in many different applications (around 900 citation). He is also the founder and the leader of PPSF project. Moreover, he has been awarded as the Most Cited Chinese Researcher in 2018, 2019, 2020, and 2021 by Elsevier/Scopus and Top-2% Scientist in 2019, 2020, 2021, and 2022 respectively by Stanford University. He is the Fellow of IET (FIET), ACM Distinguished Member (Scientist), and IEEE Senior Member.</p><p><br></p><p><b>Chin-Shiuh Shieh</b> received the M.S. degree in Electrical Engineering from the National Taiwan University, Taiwan, in 1991, and the Ph.D. degree in Computer Science and Engineering from National Sun Yat-Sen University, Taiwan in 2009. In August 1991, he joined the faculty of the Department of Electronic Engineering, National Kaohsiung University of Science and Technology, Taiwan, where he currently serves as an Associate Professor. His research interests include computer networking, embedded systems, computational intelligence and information security.</p><p><br></p><p><b>Mong Fong Horng</b> received his B.S., Ms. and Ph.D degrees from National Chiao Tung University and National Cheng Kung University, Taiwan in 1989, 1991 and 2003, respectively. He now is a jointly-appointed professor with the department of Electronics Engineering at National Kaohsiung University of Science and Technology, andKaohsiung Medical University, Taiwan. Till 2022, Dr. Horng had published 141 academic papers, 6 textbooks, about his research. There are 65 papers indexed by Web of Service (WoS), 116 papers indexed by EI and 125 papers indexed by Scopus. He served as the president of Taiwanese Association of Consumer Electronics (TACE), Chair of Tainan Chapter, IEEE Signal Processing Society and the editorial board member and reviewers of international journals, including Electronics, Information Sciences, Journal of Machine Learning and Cybernetics. Dr. Horng focuses his research interest on Internet of Things, Machine Learning, Computer Networks and Medical Informatics and related industrial cooperation.</p><p><br></p><p><b>Shu-Chuan Chu</b> received a Ph.D. degree in 2004 from the School of Computer Science, Engineering and Mathematics, Flinders University of South Australia. She joined Flinders University in December 2009 after 9 years at the Cheng Shiu University, Taiwan. She has been a ResearchFellow&nbsp; and&nbsp; Associate&nbsp; Professor&nbsp; in the College of Science and Engineering of Flinders University, Australia since December 2009. Currently, she is a Research Fellow with a Ph.D. advisor in the&nbsp; College of Computer Science and Engineering of Shandong University of Science and Technology from September 2019. She also serves as an editorial board member for Engineering Applications of Artificial Intelligence (EAAI), Journal of&nbsp; Internet Technology (JIT) and Research Reports on Computer Science (RRCS). Her research interests are mainly in Evolutionary Computation, Intelligent Computing, Information Hiding and Wireless Sensor Networks.</p><div><br></div><p></p>

This second volume of conference proceedings contains selected papers presented at ICGEC 2023, the 15th International Conference on Genetic and Evolutionary Computing, held on October 6-8, 2023 in Kaohsiung, Taiwan. The conference is intended as an international forum for the researchers and professionals in all areas of genetic and evolutionary computing. And the readers may know the up-to-date techniques of the mentioned topics, including technologies for next-generation network environments, recent progress in computational electromagnetic dynamics, future cyber security, privacy and forensics for advanced systems, data mining techniques and its applications, optimization models in deep learning and machine learning. It will&nbsp;help readers bring new ideas or apply the designed approaches from the collected papers to their professional jobs.<p></p><p></p>

Presents latest research from the 15th International Conference on Genetic & Evolutionary Computing (ICGEC 2023) Brings the new techniques and systems from the famous researchers, scholars, and professionals Provides the swarm intelligence, artificial intelligence, information hiding, big data methods