Linear Genetic Programming, NaÃ¯ve Bayes algorithm - Their Applications in Geotechnical Engineering
AbstractModeling soil and rock pose challenges due to uncertainties in their complex behavior. In the present study, linear genetic programming and NaÃ¯ve 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 Bayesâ€™ theorem with the â€œnaiveâ€ assumption of independence among all the sets of the features. Accuracy of results of classification for linear genetic programming, NaÃ¯ve Bayes were found to be 94.12% and 90.59% respectively.
 Anthony T.C. Goh, S.H. Goh, â€œSupport vector machines: Their use in geotechnical engineering as illustrated using seismic liquefaction dataâ€ Computers and Geotechnics. 34 (2007) 410â€“421.
 Amir Hossein Gandomi, Amir Hossein Alavi, â€œA new multi-gene genetic programming approach to non-linear system modeling. Part II: geotechnical and earthquake engineering problemsâ€. Neural Computing and Applications. (2012) 21:189â€“201
 Efstratios F. Georgopoulos et al. "Genetic Programming Modeling and Complexity Analysis of the Magnetoencephalogram of Epileptic Patients". Information Systems Development. 2009. 40. 383-391.
 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 Full Article:
How to Cite
LicenseAuthors who publish with this journal agree to the following terms:
- Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under aÂ Creative Commons Attribution Licensethat allows others to share the work with an acknowledgement of the work''s authorship and initial publication in this journal.
- Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal''s published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgement of its initial publication in this journal.
- Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (SeeÂ The Effect of Open Access).