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CALL FOR PAPERS
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CEC-2019 Workshop on Data-driven Optimization and Applications (DDOA 2019)

 

http://www.dscil.cn/cfps/cec2019-workshop-cfps.html

 

 

Scope and aim:

Not all objective functions can be formulated using explicit equations, instead, they are normally evaluated using high precise simulation or computationally expensive experiments. So although meta-heuristic algorithms, including evolutionary algorithms and swarm optimization, have been paid more and more attention in real-world applications, they are limited for optimizing those problems with time-expensive performance evaluation on the design. Recently, the historical data are proposed to be utilized to train surrogate models using machine learning techniques in order to replace the compute-expense/time-expensive objective function during phases of the heuristic search. The successful applications can be found in aerodynamic design, structural design, drug design, and so on.

Despite surrogate-assisted meta-heuristic algorithms get successful application, there still remain many challenges for researchers to explore. For example, due to the curse of dimensionality and the insufficiency of the samples for model training, it is very difficult, if not impossible to train accurate surrogate models. Thus, appropriate model management techniques based on the characteristics of meta-heuristic algorithms play significant important role in the surrogate-assisted optimization. In addition, modern data analytics involving advance sampling techniques and learning techniques such as semi-supervised learning, transfer learning and active learning are highly beneficial for speeding up evolutionary search while bringing new insights into the problems of interest. Finally, the application problems for verifying the efficiency and effectiveness of different approaches are also indispensability.

This workshop aims to promote the research on data-driven optimization and extend meta-heuristic algorithms to solve time-expensive problems. 

 

Topics of interest:

The topics of this workshop include but are not limited to the following topics:

• Surrogate-assisted meta-heuristic algorithms for computationally expensive problems
• Surrogate model management in single, multi/many-objective and constrained optimization
• Data collection approaches in surrogate-assisted optimization
• Adaptive model selection strategies and sampling for model training using active learning and statistical techniques
• Bayesian evolutionary optimization
• Approximation strategies
• Machine learning, such as deep learning for big data driven optimization
• Data-driven optimization using big data and data analytics
• Computationally efficient meta-heuristic algorithms for large scale and/or many-objective optimization problems
• Real world applications including multidisciplinary optimization

Submission:

The following information should be sent to the workshop chair, Dr. Chaoli Sun, [log in to unmask], [log in to unmask]by email.

– Theme of Workshop: XXXXXX

– Name of presenter

– Title

– Abstract paper and full paper.

The submission format for the paper is the same as of CEC2019, at http://www.cec2019.org/papers.html#submission

In order to participate this workshop, full or student registration of CEC 2019 is needed.

Important Dates:

Paper submission: 15 March 2019

Decision notification: 31 March 2019

Final submission: 15 April 2019

 

Workshop organizers
Prof. Chaoli Sun, Taiyuan University of Science and Technology, Taiyuan, China

Prof. Handing Wang, School of Artificial Intelligence, Xidian University, China

Dr. Tinkle Chugh, Department of Computer Science, University of Exeter, UK

Prof. Yaochu Jin, Department of Computer Science, University of Surrey 

Contact
Please feel free contact any of the special session organizers [log in to unmask] in case you have any questions about the workshop.