A genetic algorithm with modified tournament selection and efficient deterministic mutation for evolving neural network

  • Authors:

  • Dong-Sun Kim;Hyun-Sik Kim;Duck-Jin Chung

  • Affiliations:

  • DxB·Communication Convergence Research Center, Korea Electronics Technology Institute, Gyeonggi-Do, Korea;DxB·Communication Convergence Research Center, Korea Electronics Technology Institute, Gyeonggi-Do, Korea;Information Technology and Telecommunications, Inha University, Incheon, Korea

  • Venue:
  • ISNN'06 Proceedings of the Third international conference on Advances in Neural Networks - Volume Part I
  • Year:
  • 2006

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Abstract

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.