Prediction of compressive and tensile strength of limestone via genetic programming

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Prediction of shear strength parameters of soils using artificial neural networks and multivariate regression methods (2012, methodology): “Baykasoglu et al. (2008) used genetic programming for prediction of compressive and tensile strength of Gaziantep limestone.”
Machine learning in geosciences and remote sensing (2016, background): “Perhaps, one of the pioneer studies in the ﬁ eld was done by Baykasoglu et al. (2008).”
Artificial Neural Network (ANN) and Regression Tree (CART) applications for the indirect estimation of unsaturated soil shear strength parameters (2014, background): “Baykasoğlu et al. (2008) used genetic programming for the prediction of compressive and tensile strength of Gaziantep limestone.”
Energy-based numerical models for assessment of soil liquefaction (2012, methodology): “The parameters were selected on the basis of both previously suggested values (Francone, 2001; Mukkamala et al., 2004; Baykasoglu et al., 2008; Gandomi et al., 2010a,b) and making several preliminary runs and observing the performance behavior.”
Assessment of artificial neural network and genetic programming as predictive tools (2015, methodology): “GP algorithm parameter selection will affect the model generalization capability of GP; therefore, in this study, these parameters were selected based on previously suggested values in the literature [9,24].”