Linear Genetic Programming, NaÃ¯ve Bayes algorithm - Their Applications in Geotechnical Engineering - M, Vishweshwaran; ., . (International Journal of Engineering and Technology, 2018)
Modeling soil and rock pose challenges due to uncertainties in their complex behavior.Â 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.