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Generalized Net Models of Basic Genetic Algorithm Operators

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Imprecision and Uncertainty in Information Representation and Processing

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 splicetechnique), 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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Correspondence to Tania Pencheva .

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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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