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Special Session Chairs |
Dr Shengxiang Yang
Dept. of Computer Science
University of Leicester, UK
s.yang@mcs.le.ac.uk
Dr Hans-Georg Beyer
Dept. of Computer Science
Vorarlberg University of Applied Sciences, Austria
Hans-Georg.Beyer@fhv.at
Dr Yaochu Jin
Honda Research Institute Europe, Germany
yaochu.jin@honda-ri.de
Dr Ponnuthurai N. Suganthan
School of Electrical & Electronic Engineering
Nanyang Tech. University, Singapore
epnsugan@ntu.edu.sg
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Program Committee |
Hussein A. Abbass (Australia)
Dirk Arnold (Canada)
Thomas Bartz-Beielstein (Germany)
Tim Blackwell (UK)
Peter A. N. Bosman (The Netherlands)
Juergen Branke (Germany)
Hui Cheng (UK)
Ernesto Costa (Portugal)
Kalyanmoy Deb (India)
Andries P. Engelbrecht (South Africa)
Chi-Keong Goh (Singapore)
Xiaodong Li (Australia)
Ronald Morrison (USA)
Ferrante Neri (Finland)
Yew Soon Ong (Singapore)
William Rand (USA)
Khaled Rasheed (USA)
Hendrik Richter (Germany)
Philipp Rohlfshagen (UK)
Kay Chen Tan (Singapore)
Renato Tinos (Brazil)
Sima Uyar (Turkey)
Karsten Weicker (Germany)
Qingfu Zhang (UK)
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Important Dates |
Submission Deadline:
Nov 1, 2008
Author Notification:
Jan 16, 2009
Camera-ready Papers:
Feb 16, 2009
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Call for Papers |
Special Session on Evolutionary Computation in Dynamic and
Uncertain Environments (ECiDUE'09)
May 18 - 21, 2009, Trondheim, Norway
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Many real-world optimization problems are subjected
to dynamic and uncertain environments 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 may also perturb or change over time. For
these dynamic and uncertain optimization problems, the objective of
the evolutionary algorithm 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 optimally
in the presence of uncertainties. This poses serious challenges to
conventional evolutionary algorithms.
Handling dynamic and uncertain optimization problems in evolutionary
computation has received an increasing research interests over the recent
years. A variety of methods have been reported across a broad range of
application backgrounds. This special session aims at bringing researchers
from academia and industry together 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
- Tracking moving optima
- Dynamic multi-objective optimization
- Adaptation, learning, and anticipation
- Handling noisy fitness functions
- Using fitness approximations
- Searching for robust optimal solutions
- Comparative studies
- Hybrid approaches
- Theoretical analysis
- Real-world applications
Paper Submission:
Manuscripts should be prepared according to the standard format and page
limit of regular papers specified in CEC2009 and submitted through the
website
http://www.cec-2009.org/submission.shtml.
Special session papers will be treated in the same way as regular papers
and included in the conference proceedings.
Authors are encouraged to participate in the CEC2009 competition on
EC in Dynamic and
Uncertain Environments associated with the special session.
Accepted papers that involve Memetic Computing are also encouraged
to be extended for submission to the Thematic
Issue on Memetic Computing in the Presence of Uncertainties for the
journal of Memetic Computing, Springer.
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