High Energy Hadronic Collisions Using Neural Network and Genetic Programming Techniques

Created by W.Langdon from gp-bibliography.bib Revision:1.3973

@Article{Moussa:2013:IJAPM,
  author =       "Moaaz A. Moussa",
  title =        "High Energy Hadronic Collisions Using Neural Network
                 and Genetic Programming Techniques",
  journal =      "International Journal of Applied Physics and
                 Mathematics",
  year =         "2013",
  volume =       "3",
  number =       "2",
  month =        mar,
  pages =        "146--151",
  keywords =     "genetic algorithms, genetic programming, artificial
                 intelligence technique, hadronic collisions, machine
                 learning (ml), multiplicity distribution, neural
                 network, pion production",
  publisher =    "IACSIT Press",
  ISSN =         "2010-362X",
  bibsource =    "OAI-PMH server at www.doaj.org",
  oai =          "oai:doaj-articles:947928d4dd975f4d3d916615eb24f5f1",
  broken =       "http://www.ijapm.org/papers/195-PM2004.pdf",
  DOI =          "DOI:10.7763/IJAPM.2013.V3.195",
  abstract =     "Artificial Intelligence (AI) techniques of artificial
                 neural networks (ANN) and evolutionary computation of
                 genetic programming (GP) have recently been used to
                 design and implement more effective models. The
                 artificial neural network (ANN) model has been used to
                 study the charged particles multiplicity distributions
                 for antiproton-neutron ( p - n - ) and proton-neutron (
                 p - n) collisions at different lab momenta. The neural
                 network model performance was also tested at
                 non-trained space (predicted) and matched them
                 effectively. The trained NN shows a good fitting with
                 the available experimental data. The NN simulation
                 results prove a solid existence in modelling hadronic
                 collisions. Genetic Programming (GP) model is a
                 flexible and powerful technique that can be used for
                 solving the same problem. In this paper, genetic
                 programming (GP) has been used to discover a function
                 that calculates the charged particles multiplicity
                 distribution of created pions for the same interactions
                 at high energies. The predicted distributions from the
                 GP-based model are compared with the available
                 experimental data. The discovered function of GP model
                 has proved to be an excellent matching with the
                 corresponding experimental data",
}

Genetic Programming entries for Moaaz A Moussa

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