The smart city concept integrates information and communication technology, and various physical devices connected to the cloud network to optimize the efficiency of city operations and services and connect to citizens. Therefore, most of real-world problems in the smart city are multimodal interface problems and/or multi-objective optimization problems involving several conflicting objectives.
On the other hand, cloud systems may even offer tens of thousands of virtual machines, terabytes of memories and exa-bytes of storage capacity. Current trend toward many-core architecture increases the number of cores even more dramatically: we may have more than a million of cores to offer extremely massive parallelization.
In this special session, we will discuss new parallel and distributed evolutionary computation in the smart city era such as implementation of massively parallel evolutionary algorithms employing cloud computing systems and services, distributed implementation of multi-objective evolutionary algorithms on many-core architectures including GPUs, new evolutionary techniques for multimodal interface problems, in term of both reduction in execution time and improvements in accuracy of the achieved solutions.
We also welcome any types of parallel and distributed evolutionary computation on any “informal” types of computing environment in this special session including the following themes.
- Implementation of massively parallel evolutionary computation in cloud computing systems and/or services
- Parallel and distributed evolutionary algorithms in the smart city
- Distributed multi-objective swarm intelligence in the smart city
- Evolutionary learning for multimodal interface problems
- Neuro-evolution for multi-task problems in the smart city
- Distributed multi/many objective evolutionary algorithms involving several conflicting objectives
- Applications of parallel and evolutionary computation techniques in the smart city
- Applications of EC and other bio-inspired paradigms to peer to peer systems, and distributed EC algorithms that use them
Organizers (Contact information):
- Daphne Teck Ching Lai, University Brunei Darussalam (firstname.lastname@example.org)
- Haruna Matsushita, Kagawa University (email@example.com)
- Juan Julián Merelo Guervós, University of Granada (firstname.lastname@example.org)
- Kazuhiro Ohkura, Hiroshima University (email@example.com)
- Keiko Ono, Ryukoku University (firstname.lastname@example.org)
- Kenya Jin'no, Tokyo City University (email@example.com)
- Mikiko Sato, Tokai University (firstname.lastname@example.org)
- Minami Miyakawa, Hosei University (email@example.com)
- Noriyuki Fujimoto, Osaka Prefecture University (firstname.lastname@example.org)
- Shiqin Yang, Shanghai Ocean University (email@example.com)
- Toshimichi Saito. Hosei University (firstname.lastname@example.org)
- Yoshiyuki Matsumura, Shinshu University (email@example.com)
- Yuji Sato, Hosei University (firstname.lastname@example.org)
- Yuji Sato
received the B.E and Ph.D. degrees in engineering from the University of Tokyo, Japan in 1981 and 1997, respectively. From 1981 to 2000, he was with Hitachi Ltd., Tokyo, Japan. In April 2000, he joined the Faculty of Computer and Information Sciences at Hosei University, Japan, as an Associate Professor, and became a Professor in April 2001. From 2007 to 2008, he was a visiting scholar at Illinois Genetic Algorithms Laboratory (IlliGAL). His current research areas include evolutionary computation on many-core architecture and evolution of machine learning techniques in design. He received the 2014 Highly Commended Paper Award of IJICC. He is a member of the IEEE Computational Intelligence Society, the IEEE Computer Society, the ACM/SIGEVO, the Japanese Society for Evolutionary Computation, and the Information Processing Society of Japan. He is also a member of program committee of GECCO since 1999.
- Noriyuki Fujimoto
received the B.E and Ph.D. degrees in engineering from Osaka University, Japan in 1992 and 2000, respectively. From 1997 to 2002, he was with the Graduate School of Engineering Science at Osaka University, Japan, as a Research Associate. In April 2002 he joined the Graduate School of Information Science and Technology at Osaka University, Japan, as an Associate Professor. In April 2008, he joined the Graduate School of Science at Osaka Prefecture University, Japan, as a Professor. From April 2016, he is a Professor of the Graduate School of Engineering at Osaka Prefecture University, Japan. His current research areas include GPU computing and Grid computing. He is a member of the IEEE Computer Society, the ACM, the Institute of Electronics, Information, and Communication Engineers, and the Information Processing Society of Japan.
- Toshimichi Saito
received the B.E., M. E., and Ph.D. degrees in electrical engineering from Keio university, Yokohama, Japan, in 1980, 1982 and 1985, respectively. In April 1989, he joined the Faculty of Engineering at Hosei University, Tokyo, Japan, as an Associate Professor, and became a Professor in April 1998. His current research interests include learning and stabilization of artificial neural networks, nonlinear dynamics in electronic systems, and engineering applications of swarm intelligence. He served in several editorial boards including the IEICE Trans. Fundamentals (1993–1997), the IEEE Trans. Circuits Syst. I (2000-2001), the IEEE Trans. Circuits Syst. II (2003-2005), and the Intl. J. Electronics and Communications (2010-2014). He is a fellow of the IEICE, a senior member of the IEEE, and a member of the JNNS.