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Erschienen in:
Fuzzy, Intuitionistic Fuzzy, What Next? Kapitel lesen Erstes Kapitel lesen

2016 | OriginalPaper | Buchkapitel

Generalized Net Models of Basic Genetic Algorithm Operators

verfasst von : Tania Pencheva, Olympia Roeva, Anthony Shannon

Erschienen in: Imprecision and Uncertainty in Information Representation and Processing

Verlag: Springer International Publishing

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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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Vorheriges Kapitel Generalized Net Model for Monitoring the Degree of Disability in Patients With Multiple Sclerosis Based on Neurophysiologic Criteria
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Metadaten
Titel
Generalized Net Models of Basic Genetic Algorithm Operators
verfasst von
Tania Pencheva
Olympia Roeva
Anthony Shannon
Copyright-Jahr
2016
Buch
Imprecision and Uncertainty in Information Representation and Processing

Print ISBN: 978-3-319-26301-4

Electronic ISBN: 978-3-319-26302-1

Copyright-Jahr: 2016

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