Please use this identifier to cite or link to this item:
Title: Uncertainties reducing techniques in evolutionary computation
Authors: Balaji, P.G. 
Srinivasan, D. 
Tham, C.K. 
Issue Date: 2007
Citation: Balaji, P.G., Srinivasan, D., Tham, C.K. (2007). Uncertainties reducing techniques in evolutionary computation. 2007 IEEE Congress on Evolutionary Computation, CEC 2007 : 556-563. ScholarBank@NUS Repository.
Abstract: Real-world applications are bound to have certain level of uncertainty inherent in them. Among this noise is one of the most predominant factors affecting the optimization process whether it is conventional or evolutionary techniques. The evolutionary optimization techniques are found to be inherently stronger and robust to noisy environments but they are robust for lower noise levels, higher noise requires corrections to be made to the algorithm. This paper attempts to provide a comprehensive overview of the different correction methods used for optimizing noisy objective functions or fitness functions that creates uncertain environment and also provide with an brief overview of the other issues involved while using evolutionary computational methods for optimizing applications in uncertain environment. © 2007 IEEE.
Source Title: 2007 IEEE Congress on Evolutionary Computation, CEC 2007
ISBN: 1424413400
DOI: 10.1109/CEC.2007.4424519
Appears in Collections:Staff Publications

Show full item record
Files in This Item:
There are no files associated with this item.


checked on Oct 3, 2022


checked on Oct 3, 2022

Page view(s)

checked on Sep 22, 2022

Google ScholarTM



Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.