Aim:
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. Next, the evolution of modern computational science, the field of evolutionary computing is shifting rapidly to the big era where optimization problems can be characterized following different complex and cross-dependent aspects: a large number of decision variables, a large number of conflicting objectives, expensive evaluation functions, simulation-dependent problem formulations, uncertain and scenario-based models, multi- disciplinary models, non-smooth and multi-modal black-box setting, etc. These characteristics give rise to difficult challenges being beyond the ability of commonly used optimization algorithms.
The purpose of this special session is to promote the design, study, and validation of generic approaches addressing the big nature of nowadays optimization problems through the investigation of appropriate evolutionary techniques that can fit in the large scale and distributed nature of modern compute facilities.
The special session will be a nice opportunity for researchers in the evolutionary and parallel optimization filed to exchange their recent ideas and advances. In this respect, we are welcoming high quality papers in all theoretical, developmental, implementation, and applied aspects. The focus is on original research contributions eliciting the main design principles that lead to efficient decentralized evolutionary search procedures that can scale with respect the available compute resources while effectively addressing the big nature of target optimization problems.
Scopes:
- Decentralized evolutionary optimization techniques and paradigms with clear parallel potential, e.g., divide-and-conquer techniques, aggregation and grouping-based algorithms, novel decomposition-based techniques in decision and objective space, novel parallel models
- Theoretical and/or empirical studies on the scalability of parallelizing evolutionary optimization algorithms, e.g., shared memory, message-passing and hybrid algorithms
- Computational studies on parallelizing evolutionary algorithms in complex settings, e.g., expensive and simulation-based optimization, surrogate-based algorithms, noisy functions, scenario-based and robust optimization
- Design, implementation and case-study of massively parallel and large scale distributed evolutionary algorithms, e.g., on GPUs, Phi Processors, Clusters, Grids. Computational investigations on the solving of real-world big optimization problems
- Distributed multi/many objective evolutionary algorithms involving several conflicting objectives
- Parallel and distributed evolutionary algorithms in the smart city
Submission deadline:
Please follow the submission deadline from the IEEE WCCI 2020 submission website.
(https://wcci2020.org)
Important Dates:
- Paper submission: 30 January, 2020
- Notification of acceptance: 15 March, 2020
- Deadline for camera-ready submission: 15 April, 2020
Organizers (Contact information):
- Yuji Sato, Hosei University (yuji@k.hosei.ac.jp)
- Bilel Derbel, University of Lille (bilel.derbel@univ-lille.fr)
- Minami Miyakawa, Shinshu University (miyakawa@shinshu-u.ac.jp)
Biography:
- 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 2015 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. - Bilel Derbel
Bilel Derbel is an Associate Professor, having a habilitation to supervise research, at the Department of Computer Science at the University of Lille, France, since 2007. He received an Engineer degree in computer science form the ENSEIRB-MATMECA engineering school in 2002. He received his PhD in computer science from the University of Bordeaux (LaBRI, France) in 2006. In 2007, he spent one year as an assistant professor at the university of Aix-Marseille, France. He is a permanent member and deputy team leader of the BONUS (Big Optimisation and Ultra Scale Computing) research group, jointly to Inria Lille-Nord Europe and CRIStAL, CNRS. He is a co-founder member of the International Associated Laboratory MODO between Shinshu Univ., Japan, and Univ. Lille, France, on 'Massive Optimization and Computational Intelligence'. He has been a program committee member of evolutionary computing conferences such as GECCO, CEC, EvoCOP, PPSN, and a regular journal reviewer in a number of reference journals in the optimization field. He is an associate editor of the IEEE Transactions on Systems Man and Cybernetics: Systems. He co-authored more than sixty scientific papers. He was awarded best paper awards in SEAL’17, ICDCN’11, and was nominated for the best paper award in PPSN’18 and PPSN’14. His research topics are focused on the design and the analysis of computationally efficient optimization algorithms, ranging from stochastic search heuristics, evolutionary algorithm, to massively parallel and distributed optimisation algorithms. His current interests are on the design of landscape-aware and adaptive evolutionary algorithms for single- and multi- objective optimization, as well as, on the design and analysis of model-assisted optimization algorithms. - Minami Miyakawa
received the M.E. and Ph.D. degrees from The University of Electro-Communications in 2013 and 2016, respectively. From 2016 to 2019, she was a research fellow in Japan Society for the Promotion of Science (JSPS) and researched at Hosei University. Currently she is an assistant professor in Shinshu University. Her research interest includes constraint-handling in evolutionary multi-objective optimization and parallelization of evolutionary computation. She received a young researcher award from IEEE Computational Intelligence Society Japan Chapter in 2013. She was an organizer of a special session on Constraint Handling in Multi-Objective Optimization at CEC2015.
CEC 2020 SS-16 Program Committee:
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Daphne Teck Ching Lai University Brunei Darussalam Juan Julián Merelo Guervós University of Granada Kazuhiro Ohkura Hiroshima University Keiko Ono Ryukoku University Lahassane Idoumghar University of Haute Alsace Masaharu Munetomo Hokkaido University Mikiko Sato Tokai University Noriyuki Fujimoto Osaka Prefecture University Omar Abdelkafi University of Lille Roberto Santana Hermida University of the Basque Country Saúl Zapotecas Martínez Universidad Autónoma Metropolitana Shiqin Yang Shanghai Ocean University Shinya Watanabe Muroran Institute of Technology Toshio Hirotsu Hosei University Yoshiyuki Matsumura Shinshu University