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Artificial Neural Network And Genetic Algorithm Hybrid Technique For Nucleus–Nucleus Collisions

Author

Listed:
  • E. El-DAHSHAN

    (Department of Physics, Faculty of Sciences, Ain Shams University, Abbassia, Cairo, Egypt)

  • A. RADI

    (Department of Physics, Faculty of Sciences, Ain Shams University, Abbassia, Cairo, Egypt)

  • M. Y. El-BAKRY

    (Department of Physics, Faculty of Education, Ain Shams University, Egypt)

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 forC12,O16,Si28, andS32on 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 GA–ANN simulation results prove a strong presence modeling in heavy ion collisions.

Suggested Citation

  • E. El-DAHSHAN & A. RADI & M. Y. El-BAKRY, 2008. "Artificial Neural Network And Genetic Algorithm Hybrid Technique For Nucleus–Nucleus Collisions," International Journal of Modern Physics C (IJMPC), World Scientific Publishing Co. Pte. Ltd., vol. 19(12), pages 1787-1795.
    • Handle: RePEc:wsi:ijmpcx:v:19:y:2008:i:12:n:s0129183108013382
    DOI: 10.1142/S0129183108013382
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