CEC 2021 Special Session

Data-Driven Evolutionary Optimization of Computationally Expensive Problems

Theme

Meta-heuristic algorithms, including evolutionary algorithms and swarm optimization, face challenges when solving time-consuming problems, as typically these approaches require thousands of function evaluations to arrive at solutions that are of reasonable quality. Surrogate models, which are computationally cheap, have in recent years gained in popularity in assisting meta-heuristic optimization, by replacing the compute-expense/time-expensive problem during phases of the heuristic search. However, due to the curse of dimensionality, it is very difficult, if not impossible to train accurate surrogate models. Thus, appropriate model management techniques, memetic strategies and other schemes are often indispensable. 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. This special session aims at bringing together researchers from both academia and industry to explore future directions in this field.

Scope

The scope of this special session includes but is not limited to:

Surrogate-assisted evolutionary optimization for computationally expensive problems.

Adaptive sampling using machine learning and statistical techniques.

Surrogate model management in evolutionary optimization.

Data-driven optimization using big data and data analytics.

Knowledge acquisition from data and reuse for evolutionary optimization.

Multitask and Transfer Learning for optimization of computationally expensive problems.

Coping with the uncertainty inherent in computationally expensive data-driven optimization.

Computationally efficient evolutionary algorithms for large scale and/or many-objective optimization problems.

Real-world applications including multidisciplinary optimization.

Important Dates

  • Paper Submission Deadline: 31 January 2021
  • Paper Acceptance Notification: 22 March 2021
  • Final Paper Submission Deadline: 7 April 2021
  • Early Registration Deadline: 7 April 2021
  • Conference Dates: 28 June 2021 – 1 July 2021

Organizers:

  • Ran Cheng

Department of Computer Science and Engineering, Southern University of Science and Technology, Shenzhen, China

Email: ranchengcn@gmail.com

Homepage: http://emi.sustech.edu.cn/?p=4665

  • Jonathan Fieldsend

Department of Computer Science, University of Exeter, Exeter, Devon EX4, UK

Email: J.E.Fieldsend@exeter.ac.uk

Homepage: http://emps.exeter.ac.uk/computer-science/staff/jefields

  • Abhishek Gupta

Singapore Institute of Manufacturing Technology (SIMTech), Singapore

Email: abhishek_gupta@simtech.a-star.edu.sg

Homepage: https://sites.google.com/site/abhishekguptamemecomput/