Special Session Chairs

Prof. Changhe Li:
China University of Geosciences,
Wuhan, China
Email: changhe.lw@gmail.com

Dr. Michalis Mavrovouniotis:
University of Cyprus,
Nicosia, Cyprus
Email: mavrovouniotis.michalis@ucy.ac.cy

Prof. Shengxiang Yang:
De Montfort University,
Leicester, United Kingdom
Email: syang@dmu.ac.uk

Important Dates

Submission Deadline:
7 January, 2019

Notification of Acceptance:
7 March, 2019

Final Paper Submission:
31 March, 2019

Sponsors

IEEE Task Force on Evolutionary Computation in Dynamic and Uncertain Environments

Program Committee

Enrique Alba University of Malaga, Spain
Hans-Georg Beyer Vorarlberg University of Applied Sciences, Austria
Juergen Branke University of Warwick, UK
Zixing Cai Central South University, China
Ernesto Costa University of Coimbra, Portugal
Andries Engelbrecht University of Pretoria, South Africa
Sima Etaner-Uyar Istanbul Technical University, Turkey
Steffen Finck Vorarlberg University of Applied Sciences, Austria
Shouyong Jiang Newcastle University, UK
Chi-Keong Goh Advanced Technology Centre Rolls-Royce, Singapore
Yaochu Jin University of Surrey, UK
Tim Hendtlass Swinburne University of Technology, Australia
Changhe Li China University of Geosciences, China
Xiaodong Li RMIT University, Australia
Wenjian Luo University of Science and Technology of China, China
Michalis Mavrovouniotis University of Cyprus, Cyprus
Ferrante Neri De Montfort University, UK
Trung Thanh Nguyen Liverpool John Moores University, UK
David Pelta University of Granada, Spain
Khaled Rasheed The University of Georgia, USA
Ke Tang Southern University of Science and Technology, China
Chuan-Kang Ting National Chung Cheng University, Taiwan
Renato Tinos Universidade de Sao Paulo (USP), Brazil
Xin Yao Southern University of Science and Technology, China
Shengxiang Yang De Montfort University, UK

 

Call for Papers

  Special Session on Evolutionary Computation in Dynamic and Uncertain Environments (ECiDUE)
 
  
10-13 JUNE, 2019, WELLINGTON, NEW ZEALAND

Many real-world optimization problems are subject to dynamism and uncertainties that are often impossible to avoid in practice. For instance, the fitness function is uncertain or noisy as a result of simulation/ measurement errors or approximation errors (in the case where surrogates are used in place of the computationally expensive high-fidelity fitness function). In addition, the design variables or environmental conditions can be perturbed, or they change over time.

The tools to solve these dynamic and uncertain optimization problems (DOP) should be flexible, able to tolerate uncertainties, fast to allow reaction to changes and adaptation. Moreover, the objective of such tools is no longer to simply locate the global optimum solution, but to continuously track the optimum in dynamic environments, or to find a robust solution that operates properly in the presence of uncertainties.

The last decade has witnessed increasing research efforts on handling dynamic and uncertain optimization problems using evolutionary algorithms and other metaheuristics, e.g., ant colony optimization, particle swarm optimization, artificial bee colony etc., and a variety of methods have been reported across a broad range of application backgrounds.

This special session aims at bringing together researchers from both academia and industry to review the latest advances and explore future directions in this field. Topics of interest include but are not limited to:

  • Benchmark problems and performance measures
  • Dynamic single - and multi-objective optimization
  • Dynamic constrained optimization
  • Adaptation, learning, and anticipation
  • Models of uncertainty and their management
  • Handling noisy fitness functions
  • Using fitness approximations
  • Searching for robust optimal solutions
  • Algorithm comparison and benchmarking
  • Hybrid approaches
  • Theoretical analysis
  • Real-world applications