Please use this identifier to cite or link to this item: https://doi.org/10.1109/CEC.2007.4424519
Title: Uncertainties reducing techniques in evolutionary computation
Authors: Balaji, P.G. 
Srinivasan, D. 
Tham, C.K. 
Issue Date: 2007
Source: 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
URI: http://scholarbank.nus.edu.sg/handle/10635/72119
ISBN: 1424413400
DOI: 10.1109/CEC.2007.4424519
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