Faculty of Engineering, Shinshu University, Japan
Kiyoshi Tanaka is a full professor in the academic assembly (Institute of Engineering) of Shinshu University. He is the Vice-President as well as the director of Global Education Center (GEC) of the same university. He received his B.S and M.S. degrees in Electrical Engineering and Operations Research from National Defense Academy, Yokosuka, Japan, in 1984 and 1989, respectively. In 1992, he received Dr. Eng. degree from Keio University, Tokyo, Japan. In 1995, he joined the Department of Electrical and Electronic Engineering, Faculty of Engineering, Shinshu University, Nagano, Japan. He is a project leader of JSPS Strategic Young Researcher Overseas Visits Program for Accelerating Brain Circulation entitled Global Research on the Framework of Evolutionary Solution Search to Accelerate Innovation started from 2013.
His research interests include image and video processing, 3D point cloud processing, information hiding, human visual perception, evolutionary computation, multi-objective optimization, smart grid, and their applications. He received IEVC2010 Best Paper Award from IIEEJ, iFAN2010 Best Paper Award from SICE, GECCO2011 Best Paper Award and GECCO2015 Best Paper Award from ACM-SIGEVO, ISPACS2011 Best Paper Award from IEEE, Excellent Journal Paper Award from IIEEJ two times, in 2012 and in 2014, and Best Journal Paper Award from JSEC in 2012.
He is a fellow of IIEEJ (The Institute of Image Electronics Engineers of Japan). He is a member of IEEE, IEICE, IPSJ and JSEC. He is the former editor in chief of Journal of the Institute of Image Electronics Engineers Japan as well as IIEEJ Transactions on Image Electronics and Visual Computing.
Evolutionary Many-objective Optimization and Some Real-World Applications
Multi-objective evolutionary algorithms (MOEAs) are widely used in practice for solving multi-objective design and optimization problems. Historically, most applications of MOEAs have dealt with two and three objective problems, leading to the development of several evolutionary approaches that work successfully in these low dimensional objective spaces. Recently, there is a growing interest in industry to solve problems that require the simultaneous optimization of four or more objectives, known as many-objective optimization problems. However, conventional MOEAs scale up poorly with the number of objectives of the problem.
The development of robust, scalable, many-objective optimizers is an ongoing effort and a promising line of research. Critical to the development of such algorithms is an understanding of fundamental features of many-objective landscapes and the interaction between selection, variation, and population size to appropriately support the evolutionary search in high-dimensional spaces.
This talk will give an introduction to evolutionary many-objective optimization, discussing some characteristics of many-objective landscapes and relating them to working principles, performance and behavior of the optimizers. It will also present a general overview of the approaches to many-objective optimization, together with their state-of-the-art algorithms and techniques. Further, it will illustrate the use of many-objective optimization for some real-world applications.