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Levenick, J.R (1991) Inserting Introns Improves Genetic Algorithm Success Rate: Taking a Cue from Biology. Proceedings on the 4th International Conference on Genetic Algorithms.

has been cited by the following article:

• TITLE: Modeling and Simulation for High Energy Sub-Nuclear Interactions Using Evolutionary Computation Technique

  AUTHORS: Mahmoud Y. El-Bakry, El-Sayed A. El-Dahshan, Amr Radi, Mohamed Tantawy, Moaaz A. Moussa

  KEYWORDS: Modeling, Simulation, Evolutionary Computation, Genetic Programming, Hadron-Nucleus Interaction, Rapidity Distribution

  JOURNAL NAME: Journal of Applied Mathematics and Physics, Vol.4 No.1, January 13, 2016

  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 hadron nucleus (h-A) interactions through discovering functions. In this article, GP is used to simulate the rapidity distribution 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 distribution as a function of the lab momentum , 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.