2005 IEEE Intelligent Data Acquisition and Advanced Computing Systems: Technology and Applications 2005
DOI: 10.1109/idaacs.2005.282956
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Combining Clonal Selection Algorithm and Gene Expression Programming for Time Series Prediction

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Cited by 11 publications
(3 citation statements)
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“…The TSP is another promising area for CSA (Zhu et al 2007;Zhao et al 2008;). Besides these studies, researchers, such as Cutello et al (2005) Yu and Hou (2004), Kim and Bentley (2002), Brownlee (2005), Cutello and Nicosia (2006), Litvinenko et al (2005), and Panigrahi et al (2007) assess the performance of CSA versions on various problem types. Figure 2 provides the summary of problem types that are commonly solved using canonical CSA.…”
Section: Problem Types Solved With Csamentioning
confidence: 99%
“…The TSP is another promising area for CSA (Zhu et al 2007;Zhao et al 2008;). Besides these studies, researchers, such as Cutello et al (2005) Yu and Hou (2004), Kim and Bentley (2002), Brownlee (2005), Cutello and Nicosia (2006), Litvinenko et al (2005), and Panigrahi et al (2007) assess the performance of CSA versions on various problem types. Figure 2 provides the summary of problem types that are commonly solved using canonical CSA.…”
Section: Problem Types Solved With Csamentioning
confidence: 99%
“…C GE P I and C GE P M are determined by C GE P O . We further transform (15) and (16) to (17) and (18).…”
Section: N E Ge P G = Fc (C E Ge P O C E Ge P I C E Ge P M )mentioning
confidence: 99%
“…It incorporates ideas of natural evolution derived from genetic algorithm (GA) and evolution of computer programs, which comes from genetic programming (GP) [5]. Since its origination GEP has been extensively studied and applied to many problems such as: time series prediction [7] [14], classification [12][13] and linear regression [3]. GEP evolves a population of computer programs subjected to genetic operators, which leads to population diversity by introducing a new genetic material.…”
Section: A Overviewmentioning
confidence: 99%