Prof. Hisao Ishibuchi received the BS and MS degrees from Kyoto University in 1985 and 1987, respectively. He received the Ph. D. degree from Osaka Prefecture University in 1992. Since 1987, he has been with Osaka Prefecture University as a research associate (1987-1993), an assistant professor (1993), an associate professor (1994-1999) and a full professor since 1999. His research interests include evolutionary multiobjective optimization, fuzzy genetics-based classifier design, and evolutionary games. He received a Best Paper Award from GECCO 2004, HIS-NCEI 2006, FUZZ-IEEE 2009, WAC 2010, SCIS & ISIS 2010 and FUZZ-IEEE 2011. He also received a 2007 JSPS Prize from the Japan Society for the Promotion of Science. He was the IEEE CIS Vice-President for Technical Activities (2010-2013), the General Chair of ICMLA 2011, the Program Chair of CEC 2010 and IES 2014, and a Program Co-Chair of Fuzzy IEEE 2011-2013, 2015 and CEC 2013-2014. Currently, he is the Editor-in-Chief of IEEE CI Magazine (2014-2015), an Associate Editor of IEEE TFS (2004-2014), IEEE TEVC (2007-2014), IEEE Access (2013-2014) and IEEE TCyb (2013-2014), and an IEEE CIS AdCom member (2014-2016). He is an IEEE Fellow. According to Google Scholar, the total number of citations of his publications is 15,000 and his h-index is 53.
This talk starts with a brief introduction to evolutionary multiobjective optimization (EMO) where some basic ideas in EMO algorithms are explained. A basic framework of well-known Pareto dominance-based EMO algorithms is also explained. Nest we discuss why many-objective optimization problems are difficult for EMO algorithms. Difficulties can be classified into the following six categories: (i) difficulties in the search for Pareto optimal solutions, (ii) difficulties in the approximation of the entire Pareto front, (iii) difficulties in the presentation of obtained solutions, (iv) difficulties in the choice of a single final solution, (v) difficulties in the evaluation of a solution set, (vi) difficulties in the observation of search behavior. Various ideas have been proposed to address those difficulties in the field of evolutionary many-objective optimization. After each difficulty is briefly explained, some hot research topics are illustrated in detail together with future directions.
Prof. Sung-Bae Cho (S’88-M’93-SM’06) received the Ph.D. degree in computer science from KAIST (Korea Advanced Institute of Science and Technology), Taejeon, Korea, in 1993. He was an Invited Researcher of Human Information Processing Research Laboratories at Advanced Telecommunications Research (ATR) Institute, Kyoto, Japan from 1993 to 1995, and a Visiting Scholar at University of New South Wales, Canberra, Australia in 1998. He was also a Visiting Professor at University of British Columbia, Vancouver, Canada from 2005 to 2006, and at King Mongkut’s University of Technology Thonburi, Bangkok, Thailand in 2013. Since 1995, he has been a Professor in the Department of Computer Science, Yonsei University, Seoul, Korea. His research interests include hybrid intelligent systems, soft computing, evolutionary computation, neural networks, pattern recognition, intelligent man-machine interfaces, and games. He has published over 230 journal papers, and over 680 conference papers.
Dr. Cho has been serving as an associate editor for several journals including IEEE Transactions on CI and AI on Games (2009-present) and IEEE Transactions on Fuzzy Systems (2013-present). He was also the chair of Games Technical Committee, IEEE CIS (2009-2010), and Student Games-based Competition Subcommittee, IEEE CIS (2011-2012). He is a member of Board of Government (BoG) of Asia Pacific Neural Networks Assembly (APNNA) (2011-present), and a member of three technical committees in IEEE CIS such as Emergent Technologies, Computational Finance and Economics, and Games. Dr. Cho has been awarded several best paper prizes from IEEE Korea Section (1990), Korea Information Science Society (1993, 2005), International Conference on Soft Computing (1996, 1998), World Automation Congress (1998), International Conference on Information Networking (2001), and International Conference on Hybrid AI Systems (2011). He was also the recipient of the Richard E. Merwin prize from IEEE Computer Society in 1993.
Smart phones have recently incorporated diverse and powerful sensors, which include GPS receivers, microphones, cameras, light sensors, temperature sensors, digital compasses, magnetometers and accelerometers. Because of the small size and the superior computing power of the smartphones, they can become powerful sensing devices that can monitor a user's context in real time. One of the most important user contexts is location. Proper services and information can be delivered according to user’s current or future location. In this talk, I present an intelligent location-aware system for the smartphone by exploiting several machine learning techniques. The system which manages location information and predicts future places consists of location extraction, location recognition, departure time prediction, and destination prediction. Experimental results with the real log data for 10 users for five months confirm the usefulness of the developed system.