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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)

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.

Keywords

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.

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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

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