Application of gene expression programming for proton-proton interactions at large hadrons collider

This paper describes how to use gene expression programming (GEP) as an evolutionary computational optimization approach. GEP, as a machine learning technique is usually used for modeling physical phenomena by discovering a new function. In case of modeling the p-p interactions at large hadrons collider (LHC) experiments, GEP is used to simulate and predict the number of charged particles multiplicity 〈 n 〉 and total cross-section, σ _T, as a function of total center-of-mass energy, s. Considering the discovered function for 〈 n 〉 (s), the general trend of the predicted values shows good agreement at LHC [predicted values are 31.3251, 32.8638 and 35.3520 at s = 8 TeV, s = 10 TeV and s = 14 TeV respectively]. The discovered function, trained on experimental data of particle data group shows a good match as compared with the other models. The predicted values of cross section at s = 8, 10 and 14 TeV are found to be 101.0417, 105.0690 and 111.3407 mb respectively. Moreover, those predicted values are in good agreement with those reported by Block, Cudell and Nakamura.