The PPSN 2022 Workshop on Parallelism in Knowledge Transfer
To be held in conjunction with
The 17th International Conference on Parallel Problem Solving from Nature (PPSN XVII)
September 10-14, 2022
Abhishek Gupta, Amiram Moshaiov, Yaochu Jin
The utilization of knowledge from past experiences is common to the process of learning and to problem-solving by humans. Inspired by humans, researchers in computational intelligence have been developing transfer learning and transfer optimization techniques (TL&TO). TL refers to transfer of knowledge in machine learning techniques to improve performance under limited training data, whereas TO refers to such transfer for accelerating convergence rates in the search for optimal solutions under some criteria. In the last two decades various TL techniques have been studied and their effectiveness has been demonstrated over a large set of problems. This success has been followed with similar attempts in the area of TO, with a particular emphasis on population based bio-inspired optimization approaches.
The main goal of this workshop is to provide a meeting place for PPSN participants who are interested in research on bio-inspired TL&TO techniques. We aim to discuss current research on the development of such techniques and on their real-life applications. Finally, we expect to suggest future research directions for TL&TO.
The scope of this workshop includes topics such as:
· Single/Multi-objective search and optimization algorithms with transfer capability for continuous or combinatorial optimization including multi-modal optimization.
· Theoretical studies that enhance our understandings on transfer learning and optimization.
· Transfer learning and optimization using big data and data analytics.
· Transfer evolutionary optimization and learning for dynamic optimization problems.
· Transfer evolutionary optimization with domain adaptation and domain generalization.
· Hybridization of evolutionary computation and neural networks, and fuzzy systems for transfer learning and optimization.
· Hybridization of evolutionary computation and machine learning, information theory, statistics, etc., for transfer learning.
· TL&TO algorithms that are tailored for parallel processing
· Interactive TL&TO
· Real-world applications, e.g. expensive and complex optimization, text mining, computer vision, image analysis, face recognition, etc.
We invite authors/participants to submit new ideas, positional statements, and reviews/summaries/comments on Parallelism in Knowledge Transfer.
Submissions should be in the form of extended abstracts (2-4 page abstract PDF file in LNCS format).
Please send your submission to email@example.com
The PPSN 2022 conference will be held “on-site” only. A hybrid format is not planned. In case of pandemic-caused restrictions, the event will be switched to a full online format.
About the organizers
Abhishek Gupta (Senior Member, IEEE) received the PhD degree in Engineering Science from the University of Auckland, New Zealand, in 2014. He is currently a Scientist and Technical Lead in the Singapore Institute of Manufacturing Technology, a research institute in Singapore’s Agency for Science, Technology and Research (A*STAR). He also holds a joint appoint as a Research Scientist at the Data Science and Artificial Intelligence Research Center of the Nanyang Technological University. Abhishek has diverse research experience in computational science, ranging from topics in engineering sciences to computational intelligence. Currently, his main research interests lie in the theory and algorithms of transfer and multitask learning for optimization, neuro-evolution, surrogate modeling, and scientific machine learning. Abhishek is the recipient of the 2019 IEEE Transactions on Evolutionary Computation Outstanding Paper Award for his work on evolutionary multitasking. He received the IEEE Transactions on Emerging Topics in Computational Intelligence 2021 Outstanding Associate Editor Award. He is also editorial board member of the Complex & Intelligent Systems journal, the Memetic Computing journal, and the Springer book series on Adaptation, Learning, and Optimization.
Amiram (Ami) Moshaiov is a faculty member of the School of Mechanical Engineering, and of the Sagol School of Neuroscience at Tel-Aviv University (TAU). Previously, he was a faculty member at MIT, USA. He was an Associate Editor of the IEEE Transactions on Emerging Topics in Computational Intelligence, and of the Journal of Memetic Computing. In addition, he was a member of many program committees of scientific conferences, a reviewer to many scientific journals, and a member of the Management Board of the European Network of Excellence in Robotics. He is currently a member of the IEEE Task Forces on Evolutionary Deep Learning and Applications, the TF on Artificial Life and Complex Adaptive Systems, and the TF on Transfer Learning & Transfer Optimization. At TAU, Moshaiov heads a research group on computational intelligence. The main research areas of his group include: Multi-payoff Games: Theory & Evolutionary Search of Rationalizable Strategies to such Games, Multi-objective Topology and Weight Evolution of Artificial Neural-Networks, Multi-objective Optimization & Multi-Criteria Decision-Making, Multi-objective Concept Exploration, Optimization & Selection, and Multi-objective Neuro-Fuzzy Inference Systems. His research group develops computational intelligence methods which are applied to problems from a wide range of application areas.
Yaochu Jin is an Alexander von Humboldt Professor for Artificial Intelligence endowed by the German Federal Ministry of Education and Research, with the Chair of Nature Inspired Computing and Engineering, Faculty of Technology, Bielefeld University, Germany. He is also a Distinguished Chair, Professor in Computational Intelligence, Department of Computer Science, University of Surrey, Guildford, U.K. He was a “Finland Distinguished Professor” of University of Jyväskylä, Finland, “Changjiang Distinguished Visiting Professor”, Northeastern University, China, and “Distinguished Visiting Scholar”, University of Technology Sydney, Australia. His main research interests include evolutionary optimization, evolutionary learning, trustworthy machine learning, and evolutionary developmental systems.
Prof Jin is presently the Editor-in-Chief of Complex & Intelligent Systems. He was the Editor-in-Chief of the IEEE TRANSACTIONS ON COGNITIVE AND DEVELOPMENTAL SYSTEMS, an IEEE Distinguished Lecturer in 2013-2015 and 2017-2019, and the Vice President for Technical Activities of the IEEE Computational Intelligence Society (2015-2016). He was the General Co-Chair of the 2016 IEEE Symposium Series on Computational Intelligence and the Chair of the 2020 IEEE Congress on Evolutionary Computation. He is the recipient of the 2018 and 2021 IEEE Transactions on Evolutionary Computation Outstanding Paper Award, and the 2015, 2017, and 2020 IEEE Computational Intelligence Magazine Outstanding Paper Award. He was named by the Web of Science as “a Highly Cited Researcher” from 2019 to 2021 consecutively. He is a Member of Academia Europaea and Fellow of IEEE.