Artificial neural network and genetic algorithm hybrid technique for nucleusnucleus collisions

E. El-Dahshan, A. Radi, M. Y. El-Bakry

Research output: Contribution to journalArticle

10 Citations (Scopus)

Abstract

Selecting the optimal topology of a neural network for a particular application is a difficult task. Genetic Algorithm (GA) has been used to find the optimal neural network (NN) solution (i.e., hybrid technique) to calculate the pseudo-rapidity distribution of the shower particles for C12, O16, Si28, and S32 on nuclear emulsion. An efficient NN has been designed by GA to predict the distributions that are not present in the training set and matched them effectively. The proposed method shows a better fitting with experimental data. The hybrid technique GAANN simulation results prove a strong presence modeling in heavy ion collisions.

Original languageEnglish
Pages (from-to)1787-1795
Number of pages9
JournalInternational Journal of Modern Physics C
Volume19
Issue number12
DOIs
Publication statusPublished - Dec 2008

Fingerprint

Network Algorithms
genetic algorithms
Artificial Neural Network
Collision
Genetic algorithms
Genetic Algorithm
Neural Networks
Neural networks
collisions
Heavy-ion Collisions
nuclear emulsions
Emulsion
showers
Heavy ions
ionic collisions
Emulsions
education
topology
Topology
Experimental Data

Keywords

  • Genetic algorithm
  • Heavy ion collisions
  • Neural network

ASJC Scopus subject areas

  • Computer Science Applications
  • Computational Theory and Mathematics
  • Physics and Astronomy(all)
  • Statistical and Nonlinear Physics
  • Mathematical Physics

Cite this

Artificial neural network and genetic algorithm hybrid technique for nucleusnucleus collisions. / El-Dahshan, E.; Radi, A.; El-Bakry, M. Y.

In: International Journal of Modern Physics C, Vol. 19, No. 12, 12.2008, p. 1787-1795.

Research output: Contribution to journalArticle

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