Prof. Hussein A. Abbass
School of Engineering and Information Technology
University of New South Wales - Canberra - Australia
Email: h D abbass A adfa D edu D au
Hussein A. Abbass* and Huajin Tang**
*The University of New South Wales, School of Engineering and Information Technology, Canberra, ACT 2600, Australia.
**Institute for Infocomm Research, Agency for Science, Technology and Research (A*STAR), 1 Fusionopolis Way, #21-01 Connexis South Tower, Singapore 138632.
This special issue builds on the special session on BigOpt at IEEE CEC 2015 [Click Here].
Big Optimization (BigOpt) is the term we coin to differentiate optimization problems that rely on big data from classical large scale optimization. BigOpt problems involve thousands of variables and are normally expected to hide trends.
In the age of Big Data, there is an urge to take evolutionary optimization techniques to the next level for solving problems with hundreds, thousands, and even millions of variables. This issue takes first steps towards achieving this objective and pushes the boundaries of evolutionary optimization beyond the small-scale problems that have been used as benchmark problems in the literature so far.
In this thematic issue, several aspects of evolutionary algorithm design for big optimization in Memetic Computing will be considered, but not limited to the following:
The papers should be submitted online through the manuscript submission system of the Memetic Computing Journal [submit here]. Please select the special session from the list of topics and send an email to the Guest Editors notifying them with the submission at h.abbass AT unsw.edu.au and htang AT i2r.a-star.edu.sg
|Paper submission deadline||31 July 2015|
|Notification to authors||15 September 2015|
|Revised paper submission||31 October 2015|
|Expected publication date||2015/2016|