Please use this identifier to cite or link to this item:
|Title:||Uncertainties reducing techniques in evolutionary computation|
|Authors:||Balaji, P.G. |
|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. https://doi.org/10.1109/CEC.2007.4424519|
|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|
|Appears in Collections:||Staff Publications|
Show full item record
Files in This Item:
There are no files associated with this item.
checked on Jul 14, 2018
WEB OF SCIENCETM
checked on Jun 19, 2018
checked on Jul 6, 2018
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.