Emergent Technologies Task Force on
Collaborative Learning and Optimization
School of Computer Science and Software Engineering, Swinburne University of Technology, Melbourne, Australia
Collaborative Innovation Center for Computational Intelligence (OMEGA), Xidian University & Northwestern Polytechnical University, Xi'an, China
School of Computer Science and Technology, University of Science and Technology of China, China
Yew Soon Ong
School of Computer Science and Engineering, Nanyang Technological University, Singapore
El-ghazali Talbi, University of Lille, France
Florence Forbes, INRIA, France
Gabriela Ochoa, University of Stirling, UK
Jose A. Lozano, University of the Basque Country, Spain
Jing Liang, Zhengzhou University, China
Marc Schoenauer, INRIA, France
Swagatam Das, Indian Statistical Institute, India
Qingfu Zhang, City University of Hong Kong, Hong Kong
Shan He, University of Birmingham, UK
Thomas Bartz-Beielstein, Cologne University of Applied Science, Germany
Thomas Jansen, Aberystwyth University, UK
Xiaodong Li, RMIT University, Australia
Yaochu Jin, University of Surrey, UK
Zexuan Zhu, Shenzhen University, China
Learning and optimization are two essential tasks that computational intelligence aims to address. Numerous techniques have been developed for these two purposes separately. In fact, learning and optimization are closely related. On the one hand, learning can be formulated as a model-centric or data-centric optimization problem, and accordingly solved by optimization techniques. On the other hand, optimization can be regarded as an adaptive learning process, and thus tackled by learning methodologies. Moreover, learning techniques play a crucial role in competence analysis of optimizers, and meanwhile optimization techniques provide an effective way to analyze learners’ domains of competence.
Recent years have seen remarkable attempts at collaborative learning and optimization. For instance, learning classifier systems, evolutionary neural networks, evolutionary ensemble learning, evolutionary kernel machines, evolutionary clustering, evolutionary data generation and extraction for learning, supervised classifiers’ domain of competence analysis using evolutionary multi-objective optimization, theoretical analysis of optimization techniques using learning theory, optimization by building and using probabilistic models, self-adaptive and tuning-free optimization, ensemble optimization, statistical analysis of evolutionary computation, automatic heuristic design, etc. These research efforts have led to a great deal of cutting-edge techniques in the corresponding research fields.
Nowadays, the emergence of more and more complex problems in real-world applications insistently calls for in-depth investigations of synergy between learning and optimization. Moreover, feasibility of implementing collaborative learning and optimization techniques on massive parallel systems must be seriously taken into account to ensure that large-scale problems can be solved in a reasonable time.
The primary goal of this task force is to promote research on collaborative learning and optimization. Moreover, this task force shall provide a forum for academic and industrial researchers from both learning and optimization communities to collaboratively explore promising directions of synergy between techniques from these two areas.
The scope of this task force covers, but is not limited to:
Learning based on data-centric optimization
Learning based on model-centric optimization
Competence analysis of learners using optimization techniques
Automatic selection of learning techniques for solving real-world problems
Theoretical relationship between learning and optimization
Optimization based on state-of-the-art learning techniques
Self-adaptive and tuning-free optimization based on novel learning models
Competence analysis of optimizers using statistical & machine learning techniques
Automatic selection of optimization techniques for solving real-world problems
Collaborative learning and optimization on massively parallel systems
CFP: Swarm and Evolutionary Computation (Elsevier, SCI IF: 2.963) Special Issue on "Collaborative learning and optimization based on swarm and evolutionary computation"
Last update in 03/04/2017