A Genetic programming for modeling Hadronnucleus Interactions at 200 GeV/c

Created by W.Langdon from gp-bibliography.bib Revision:1.3872

@Misc{oai:CiteSeerX.psu:10.1.1.302.1666,
  title =        "A Genetic programming for modeling Hadronnucleus
                 Interactions at 200 {GeV/c}",
  author =       "Mahmoud Y. El-bakry and El-sayed A. El-dahshan and 
                 A. Radi and M. Tantawy",
  year =         "2013",
  month =        jul # "~23",
  keywords =     "genetic algorithms, genetic programming",
  annote =       "The Pennsylvania State University CiteSeerX Archives",
  bibsource =    "OAI-PMH server at citeseerx.ist.psu.edu",
  language =     "en",
  oai =          "oai:CiteSeerX.psu:10.1.1.302.1666",
  rights =       "Metadata may be used without restrictions as long as
                 the oai identifier remains attached to it.",
  URL =          "http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.302.1666",
  URL =          "http://www.ijser.org/researchpaper/Genetic-programming-for-modeling-Hadron.pdf",
  abstract =     "Genetic programming (GP) is a soft computing search
                 technique, which was used to develop a tree-structured
                 program with the purpose of minimising the fitness
                 value of it. It is also a powerful and flexible
                 evolutionary technique with some special features that
                 are suitable for building a tree representation which
                 is always the best solution for the problem we
                 encounter. In this paper, GP has been used to describe
                 a function that calculates charged and negative pions
                 multiplicity distribution for Hadron-nucleus
                 interactions at 200 GeV/c and also compared with the
                 parton two fireball model (PTFM). GP calculations are
                 in accordance with the available experimental data in
                 comparison with the conventional ones (PTFM). Finally,
                 the calculation results showed that the GP model is
                 superior to the traditional techniques that we have
                 ever seen so far. Index Terms --- Genetic programming
                 (GP), machine learning (ML), pion production,
                 multiplicity distribution.",
}

Genetic Programming entries for Mahmoud Y El-Bakry El-sayed A El-dahshan Amr Mohamed Mahmoud Khairat Radi M Tantawy

Citations