Categories |
OPTIMIZATION
METAHEURISTICS
EVOLUTIONARY COMPUTATION
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About |
In the past two decades, many evolutionary algorithms have been developed and successfully applied for solving a wide range of optimization problems. Although these techniques have shown excellent search capabilities when applied to small or medium sized problems, they still encounter serious challenges when applied to large scale problems, i.e., problems with several hundreds to thousands of variables. This is due to the Curse of dimensionality, as the size of the solution space of the problem grows exponentially with the increasing number of decision variables, there is an urgent need to develop more effective and efficient search strategies to better explore this vast solution space with limited computational budgets. In recent years, research on scaling up EAs to large-scale problems has attracted significant attention, including both theoretical and practical studies. Existing work on tackling the scalability issue is getting more and more attention in the last few years. |
Call for Papers |
Manuscripts should be prepared according to the standard format and page limit of regular papers specified for CEC’2021. Instructions on the preparation of the manuscripts can be obtained at the WCCI’2020 website: https://cec2021.mini.pw.edu.pl/en/calls/call-for-papers. Special session papers will be treated in the same way as regular papers and will be included in the conference proceedings. Submissions should be done by using the following link: https://cec2021.mini.pw.edu.pl/en/calls/call-for-papers. |
Credits and Sources |
[1] CEC 2021 : IEEE CEC Special Session on Large Scale Global Optimization |