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Genetic Algorithms, Floating Point Numbers And Applications

Author

Listed:
  • YORICK HARDY

    (International School for Scientific Computing, University of Johannesburg, P. O. Box 524, Auckland Park 2006, South Africa)

  • WILLI-HANS STEEB

    (International School for Scientific Computing, University of Johannesburg, P. O. Box 524, Auckland Park 2006, South Africa)

  • RUEDI STOOP

    (Institut für Neuroinformatics, ETHZ/UNIZH, Winterthurerstr. 190, 8057 Zürich, Switzerland)

Abstract

The core in most genetic algorithms is the bitwise manipulations of bit strings. We show that one can directly manipulate the bits in floating point numbers. This means the main bitwise operations in genetic algorithm mutations and crossings are directly done inside the floating point number. Thus the interval under consideration does not need to be known in advance. For applications, we consider the roots of polynomials and finding solutions of linear equations.

Suggested Citation

  • Yorick Hardy & Willi-Hans Steeb & Ruedi Stoop, 2005. "Genetic Algorithms, Floating Point Numbers And Applications," International Journal of Modern Physics C (IJMPC), World Scientific Publishing Co. Pte. Ltd., vol. 16(11), pages 1811-1816.
    • Handle: RePEc:wsi:ijmpcx:v:16:y:2005:i:11:n:s0129183105008321
    DOI: 10.1142/S0129183105008321
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    References listed on IDEAS

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    1. ,, 2004. "Problems And Solutions," Econometric Theory, Cambridge University Press, vol. 20(2), pages 427-429, April.
    2. ,, 2004. "Problems And Solutions," Econometric Theory, Cambridge University Press, vol. 20(1), pages 223-229, February.
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