A Genetic Algorithm with Modified Tournament Selection and Efficient Deterministic Mutation for Evolving Neural Network

  • Dong-Sun Kim
  • Hyun-Sik Kim
  • Duck-Jin Chung
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3971)


In this paper, we present a genetic algorithm (GA) based on tournament selection (TS) and deterministic mutation (DM) to evolve neural network systems. We use population diversity to determine the mutation probability for sustaining the convergence capacity and preventing local optimum problem of GA. In addition, we consider population that have a worst fitness and best fitness value for tournament selection to fast convergence. Experimental results with mathematical problems and pattern recognition problem show that the proposed method enhance the convergence capacity about 34.5% and reduce computation power about 40% compared with the conventional method.


Genetic Algorithm Mutation Probability Tournament Selection Gray Code Neural Network System 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Hicklin, J., Demuth, H.: Modeling Neural Networks on the MPP. In: Proceeding of 2nd Symposium on the Frontiers of Massively Parallel Computation, pp. 39–42 (1988)Google Scholar
  2. 2.
    Ho, C.W., Lee, K.H., Leung, K.S.: A Genetic Algorithm Based on Mutation and Crossover with Adaptive Probabilities. In: Proceeding of the 1999 Congress on Evolutionary Computation, pp. 768–775 (1999)Google Scholar
  3. 3.
    Sunghoon, J.: Queen-bee Evolution for Genetic Algorithms. Electron. Lett. 39(6), 575–576 (2003)CrossRefGoogle Scholar
  4. 4.
    Kim, J.J., Choi, Y.H., Lee, C.H., Chung, D.J.: Implementation of a High-Performance Genetic Algorithm Processor for Hardware Optimization. IEICE Trans. Electron (1), 195–203 (2002)Google Scholar
  5. 5.
    Hesser, M.R.: Towords an Optimal Mutation Probability for Genetic Algorithms. In: Proceedings of the first Conference on Parallel Problem Solving from Nature, pp. 23–32 (1990)Google Scholar
  6. 6.
    Gong, D., Pan, F., Sun, X.: Research on a Novel Adaptive Genetic Algorithm. In: Proceeding of the 2002 IEEE International Symposium on Industrial Electronics, pp. 357–359 (2002)Google Scholar
  7. 7.
    Yentis, R., Zaghloul, M.E.: VLSl Implementation of Focally Connected Neural Networks for Solving Partial Differential Equations. IEEE Trans. Circnits Syst. I, Fundaln. Theory Appl. 43(8), 687–690 (1996)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Dong-Sun Kim
    • 1
  • Hyun-Sik Kim
    • 1
  • Duck-Jin Chung
    • 2
  1. 1.DxB·Communication Convergence Research CenterKorea Electronics Technology InstituteGyeonggi-DoKorea
  2. 2.Information Technology and TelecommunicationsInha UniversityIncheonKorea

Personalised recommendations