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An Approach to Codification Power on the Behavior of Genetic Algorithms

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Intelligent Computing Systems (ISICS 2016)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 597))

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Abstract

Genetics Algorithms (GAs) are based on the principles of Darwins evolution which are applied to the minimization complex function successfully. Codification is a very important issue when GAs are designed to dealing with a combinatorial problem. An effective crossed binary method is developed. The GAs have the advantages of no special demand for initial values of decision variables, lower computer storage, and less CPU time for computation. Better results are obtained in comparison the results of traditional Genetic Algorithms. The effectiveness of GAs with crossed binary coding in minimizing the complex function is demonstrated.

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Author information

Authors and Affiliations

  1. Posgrado en Ciencias de la Ingeniería, Instituto Tecnológico de Tijuana, Tijuana, Mexico

    Y.El. Hamzaoui & S.A. Puga

  2. Centro de Investigación en Ingeniaría y Ciencias Aplicadas, Universidad Autónoma del Estado de Morelos, Cuernavaca, Morelos, Mexico

    J.A. Rodriguez

  3. Facultad de Ingeniería, Universidad Autónoma de Yucatán, Mérida, Yucatán, Mexico

    M.A. Escalante Soberanis & A. Bassam

Authors
  1. Y.El. Hamzaoui
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  2. J.A. Rodriguez
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  3. S.A. Puga
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  4. M.A. Escalante Soberanis
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  5. A. Bassam
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Corresponding author

Correspondence to Y.El. Hamzaoui .

Editor information

Editors and Affiliations

  1. Universidad Autónoma de Yucatán, Mérida, Yucatán, Mexico

    Anabel Martin-Gonzalez

  2. Universidad Autónoma de Yucatán, Mérida, Yucatán, Mexico

    Victor Uc-Cetina

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© 2016 Springer International Publishing Switzerland

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Hamzaoui, Y., Rodriguez, J., Puga, S., Escalante Soberanis, M., Bassam, A. (2016). An Approach to Codification Power on the Behavior of Genetic Algorithms. In: Martin-Gonzalez, A., Uc-Cetina, V. (eds) Intelligent Computing Systems. ISICS 2016. Communications in Computer and Information Science, vol 597. Springer, Cham. https://doi.org/10.1007/978-3-319-30447-2_12

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  • DOI: https://doi.org/10.1007/978-3-319-30447-2_12

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-30446-5

  • Online ISBN: 978-3-319-30447-2

  • eBook Packages: Computer ScienceComputer Science (R0)

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