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Generalized Net Models of Basic Genetic Algorithm Operators

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Part of the book series: Studies in Fuzziness and Soft Computing ((STUDFUZZ,volume 332))

Abstract

Generalized nets (GN) are applied here to describe some basic operators of genetic algorithms, namely selection, crossover and mutation and different functions for selection (roulette wheel selection method and stochastic universal sampling), different crossover techniques (one-point crossover, two-point crossover, and “cut and splice” technique), as well as mutation operator (mutation operator of the Breeder genetic algorithm). The resulting GN models can be considered as separate modules, but they can also be accumulated into a single GN model to describe a whole genetic algorithm.

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Authors and Affiliations

  1. Institute of Biophysics and Biomedical Engineering, Bulgarian Academy of Sciences, 105 Acad. G. Bonchev Str., 1113, Sofia, Bulgaria

    Tania Pencheva & Olympia Roeva

  2. Faculty of Engineering & IT, University of Technology, Sydney, NSW, 2007, Australia

    Anthony Shannon

Authors
  1. Tania Pencheva
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  2. Olympia Roeva
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  3. Anthony Shannon
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Corresponding author

Correspondence to Tania Pencheva .

Editor information

Editors and Affiliations

  1. School of Computing and Communications, Lancaster University Bailrigg, Lancaster, United Kingdom

    Plamen Angelov

  2. Intelligent Systems Laboratory, Prof. Assen Zlatarov University, Bourgas, Bulgaria

    Sotir Sotirov

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Pencheva, T., Roeva, O., Shannon, A. (2016). Generalized Net Models of Basic Genetic Algorithm Operators. In: Angelov, P., Sotirov, S. (eds) Imprecision and Uncertainty in Information Representation and Processing. Studies in Fuzziness and Soft Computing, vol 332. Springer, Cham. https://doi.org/10.1007/978-3-319-26302-1_19

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  • DOI: https://doi.org/10.1007/978-3-319-26302-1_19

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-26301-4

  • Online ISBN: 978-3-319-26302-1

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