Linear Genetic Programming, Naïve Bayes algorithm - Their Applications in Geotechnical Engineering

 
 
 
  • Abstract
  • Keywords
  • References
  • PDF
  • Abstract


    Modeling soil and rock pose challenges due to uncertainties in their complex behavior.  In the present study, linear genetic programming and Naïve Bayes are used in classification of liquefied and non-liquefied data. Soil and seismic parameters influencing the soil liquefaction potential are used to develop the models. Genetic Programming is the automatic creation of computer programs to perform a selected task using Darwinian natural selection. Linear genetic programming forms a peculiar subset of genetic programming where computer programs in a population are constituted as successive repetition of instructions from imperative programming language. Naive Bayes methods are supervised learning algorithms by applying Bayes’ theorem with the “naive” assumption of independence among all the sets of the features. Accuracy of results of classification for linear genetic programming, Naïve Bayes were found to be 94.12% and 90.59% respectively.

  • Keywords


    .

  • References


      [1] Anthony T.C. Goh, S.H. Goh, “Support vector machines: Their use in geotechnical engineering as illustrated using seismic liquefaction data” Computers and Geotechnics. 34 (2007) 410–421.

      [2] Amir Hossein Gandomi, Amir Hossein Alavi, “A new multi-gene genetic programming approach to non-linear system modeling. Part II: geotechnical and earthquake engineering problems”. Neural Computing and Applications. (2012) 21:189–201

      [3] Efstratios F. Georgopoulos et al. "Genetic Programming Modeling and Complexity Analysis of the Magnetoencephalogram of Epileptic Patients". Information Systems Development. 2009. 40. 383-391.

      [4] Frank D. Francone et al. "Discrimination of Unexploded Ordnance from Clutter Using Linear Genetic Programming". Genetic Programming Theory and Practice III. 2006. 49-64.


 

View

Download

Article ID: 16562
 
DOI: 10.14419/ijet.v7i3.12.16562




Copyright © 2012-2015 Science Publishing Corporation Inc. All rights reserved.