Journal of Applied Mathematics and Physics, 2016, 4, 53-65 Published Online January 2016 in SciRes. http://www.scirp.org/journal/jamp http://dx.doi.org/10.4236/jamp.2016.41009 How to cite this paper: El-Bakry, M.Y., El-Dahshan, E.-S.A., Radi, A., Tantawy, M. and Moussa, M.A. (2016) Modeling and Simulation for High Energy Sub-Nuclear Interactions Using Evolutionary Computation Technique. Journal of Applied Ma- thematics and Physics, 4, 53-65. http://dx.doi.org/10.4236/jamp.2016.41009 Modeling and Simulation for High Energy Sub-Nuclear Interactions Using Evolutionary Computation Technique Mahmoud Y. El-Bakry 1 , El-Sayed A. El-Dahshan 2,3 , Amr Radi 3,4 , Mohamed Tantawy 1 , Moaaz A. Moussa 1,5 1 Department of Physics, Faculty of Sciences, Ain Shams University, Cairo, Egypt 2 Egyptian E-Learning University, Giza, Egypt 3 Department of Physics, Faculty of Education, Ain Shams University, Cairo, Egypt 4 The British University in Egypt (BUE), Cairo, Egypt 5 Buraydah Colleges, East Qassim University, Buraydah, KSA Received 19 November 2015; accepted 10 January 2016; published 13 January 2016 Copyright © 2016 by authors and Scientific Research Publishing Inc. This work is licensed under the Creative Commons Attribution International License (CC BY). http://creativecommons.org/licenses/by/4.0/ Abstract High energy sub-nuclear interactions are a good tool to dive deeply in the core of the particles to recognize their structures and the forces governed. The current article focuses on using one of the evolutionary computation techniques, the so-called genetic programming (GP), to model the ha- dron nucleus (h-A) interactions through discovering functions. In this article, GP is used to simu- late the rapidity distribution       N N Y 1d d of total charged, positive and negative pions for p − -Ar and p − -Xe interactions at 200 GeV/c and charged particles for p-pb collision at 5.02 TeV. We have done so many runs to select the best runs of the GP program and finally obtained the rapidity distribu- tion       N N Y 1d d as a function of the lab momentum ( ) Lab P , mass number (A) and the number of particles per unit solid angle (Y). In all cases studied, we compared our seven discovered functions produced by GP technique with the corresponding experimental data and the excellent matching was so clear. Keywords Modeling, Simulation, Evolutionary Computation, Genetic Programming, Hadron-Nucleus Interaction, Rapidity Distribution