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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References
Abuiziah, I., Shakarneh, N.: A review of genetic algorithm optimization: operations and applications to water pipeline systems. Int. J. Phys. Nucl. Sci. Eng. 7(12), 341–347 (2013)
Atanassov, K.: Generalized Nets. World Scientific, Singapore (1991)
Atanassov, K., Aladjov, H.: Generalized Nets in Artificial Intelligence: Generalized nets and Machine Learning, vol. 2. Prof. M. Drinov Academic Publishing House, Sofia (2000)
Atanassov, K.: On Generalized Nets Theory. Prof. M. Drinov Academic Publishing House, Sofia (2007)
Baker, J.: Reducing bias and inefficiency in the selection algorithm. In: Proceedings of the Second International Conference on Genetic Algorithms and their Application, pp. 14–21. Hillsdale, New Jersey (1987)
Bies, R., Muldoon, M., Pollock, B., Manuck, S., Smith, G., Sale, M.: A genetic algorithm-based, hybrid machine learning approach to model selection. J. Pharmacokinet. Pharmacodyn. 33, 196–221 (2006)
Chipperfield A., Fleming, P.J., Pohlheim, H., Fonseca, C.M.: Genetic Algorithm Toolbox for Use with MATLAB. Technical Report No. 512, Department of Automatic Control and Systems Engineering, University of Sheffield (1994)
Crisan, C., Mühlenbein, H.: The Breeder Genetic Algorithm for Frequency Assignment. Lecture Notes in Computer Science, vol. 1498, p. 897 (1998)
Davis, L.: Handbook of Genetic Algorithms, Van Nostrand Reinhold, New York (1991)
Fogel, D.: Evolutionary Computation: Toward a New Philosophy of Machine Intelligence, 3rd edn. IEEE Press, Hoboken (2006)
Goldberg, D.E.: Genetic Algorithms in Search, Optimization and Machine Learning. Addison Wesley, Reading (1989)
Holland, J.: Adaptation in Natural and Artificial Systems. University of Michigan Press, Ann Arbor (1975)
Houck, C., Joines, J., Kay, M.: A genetic algorithm for function optimization: a Matlab implementation. http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.22.4413&rep=rep1&type=pdf. Accessed 22 Oct 2015
http://www.doc.ic.ac.uk/~nd/surprise_96/journal/vol1/hmw/article1.html. Accessed 22 Oct 2015
Krawczak, M.: A novel modeling methodology: Generalized nets. In: Lecture Notes in Computer Science, vol. 4029, p. 1160 (2006)
Larranaga, P., Karshenas, H., Bielza, C., Santana, R.: A review on evolutionary algorithms in Bayesian network learning and inference tasks, Inf. Sci. 233, 109–125 (2013)
Malhotra, R., Singh, N., Singh, Y.: Genetic algorithms: concepts, design for optimization of process controllers. Comput. Inf. Sci. 4(2), 39–54 (2011)
MathWorks: Genetic Algorithm Toolbox User’s Guide for MATLAB
Michalewicz, Z.: Genetic Algorithms + Data Structures = Evolution Programs, 2nd edn. Springer, Berlin (1994)
Montiel, O., Castillo, O., Melin, P., Sepulveda, R.: Application of a Breeder genetic algorithm for filter optimization. Nat. Comput. Int. J. Arch. 4(1), 11–37 (2005)
Montiel, O., Castillo, O., Sepulveda, R., Melin, P.: Application of a Breeder genetic algorithm for finite impulse filter optimization. Inf. Sci. 161, 139–158 (2004)
Mühlenbein, H., Schlierkamp-Voosen, D.: Predictive model for Breeder genetic algorithm. Evol. Comput. 1, 25–49 (1993)
Pencheva, T., Atanassov, K., Shannon, A.: Generalized net model of selection function choice in genetic algorithms. In: Recent Advances in Fuzzy Sets, Intuitionistic Fuzzy Sets, Generalized Nets and Related Topics, Applications, vol. II, pp. 193–201. Systems Research Institute, Polish Academy of Sciences, Warsaw (2011)
Pencheva, T., Atanassov, K.: Generalized Net Model of Simple Genetic Algorithms Modifications. In: Kacprzyk, J., Krawczak, M., Szmidt, E. (eds.) Issues in Intuitionistic Fuzzy Sets and Generalized Nets, vol. 10, pp. 97–106. Wydawnictwo WSISiZ, Warszawa (2013)
Pencheva T., Roeva, O., Shannon, A.: Generalized net models of crossover operator of genetic algorithm. In: Proceedings of Ninth International Workshop on Generalized Nets, pp. 64–70. Sofia, 04 July 4 2008
Riolo, R., McConaghy, T., Vladislavleva, E. (eds.): Genetic Programming Theory and Practice VIII (Genetic and Evolutionary Computation), 276 p. Springer (2011)
Roeva, O. (ed.): Real-World Application of Genetic Algorithms. In Tech, Rijeka (2012)
Roeva, O., Atanassov, K., Shannon, A.: Generalized net for evaluation of the genetic algorithm fitness function. In: Proceedings of the Eighth International Workshop on Generalized Nets, pp. 48–55. Sofia, 26 June 2007
Roeva, O., Pencheva, T., Shannon, A., Atanassov, K.: Generalized Nets in Artificial Intelligence, Generalized Nets and Genetic Algorithms, vol. 7. Prof. M. Drinov Academic Publishing House, Sofia (2013)
Roeva, O., Pencheva, T.: Generalized net model of a multi-population genetic algorithm. In: Kacprzyk, J., Krawczak, M., Szmidt, E. (eds.) Issues in Intuitionistic Fuzzy Sets and Generalized Nets, vol. 8, pp. 91–101. Wydawnictwo WSISiZ, Warszawa (2010)
Roeva, O., Pencheva, T., Atanassov, K.: Generalized net of a genetic algorithm with intuitionistic fuzzy selection operator. In: New Developments in Fuzzy Sets, Intuitionistic Fuzzy Sets, Generalized Nets and Related Topics, Foundations, vol. I, pp. 167–178. Systems Research Institute, Polish Academy of Sciences, Warsaw (2012)
Tasan, S.O., Tunali, S.: A review of the current applications of genetic algorithms in assembly line balancing. J. Intell. Manuf. 19(1), 49–69 (2008)
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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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