Abstract
Genetic algorithm (GA) employs real numbers (or bit strings) as genotype values for solving real-valued optimization problems. The author previously proposed an extension of GA. The proposed method extends the processes of GA to handle fuzzy numbers as genotype values so that GA can be applied to fuzzy-valued optimization problems. The author has applied the FGA to the evolution of fuzzy-valued neural networks (FNN) and showed that FGA could evolve FNNs, which model fuzzy functions well, despite that the training (evolution) of the FNNs was not supervised. In the previous paper, fuzzy numbers as the genotype values were symmetric triangular ones. Each symmetric triangular fuzzy number can be specified by its lower and upper limit values or its center and width values, and thus the FGA can employ either of two models, the lower and upper (LU) model or the center and width (CW) model for specifying genotype values. Ability of the FGA in searching solutions may depend on the model, because the crossover and the mutation operations for the fuzzy genotypes with the LU model are slightly different from those operations with the CW model. In this paper, the author compares the two models to investigate which model contributes better for the FGA to find better solutions. Application of the FGA is evolutionary training of the FNNs. An experimental result shows that the CW model contributed slightly better than the LU model in evolving FNNs which model fuzzy functions.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Goldberg, D.E.: Genetic Algorithms in Search Optimization and Machine Learning. Kluwer Academic Publishers, Norwell (1989)
Back, T.: Evolutionary Algorithms in Theory and Practice: Evolution Strategies, Evolutionary Programming, Genetic Algorithms. Oxford Univ Press, Oxford (1996)
Okada, H., Matsuse, T., Wada, T.: GA with fuzzy-valued genotypes and its application to neuroevolution. In: Proceedings of Asia Pacific Symposium of Intelligent and Evolutionary Systems (IES) 2012, pp. 15–18 (2012)
Ishibuchi, H., Tanaka, H., Okada, H.: Fuzzy neural networks with fuzzy weights and fuzzy biases. In: Proceedings of IEEE International Conferences on Neural Networks, pp. 1650–1655 (1993)
Yao, X.: Evolving artificial neural networks. In: Proceedings of the IEEE, vol. 87, no. 9, pp. 1423–1447 (1999)
Zadeh, L.A.: The concept of a linguistic variable and its application to approximate reasoning—I, II, and III. In: Information Science, vol. 8, pp. 199–249, pp. 301–357 and vol. 9, pp. 43–80 (1975)
Alefeld, G., Herzberger, J.: Introduction to Interval Computation Academic Press, New York (1983)
Eshelman, L.J., Schaffer, J.D.: Real-coded genetic algorithms and interval-schemata. In: Whitley, D.L. (ed.) Foundation of Genetic Algorithms 2, pp. 187–202 (1993)
Acknowledgment
This research was supported by Kyoto Sangyo University Research Grant.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2015 Springer Japan
About this chapter
Cite this chapter
Okada, H. (2015). Comparison of Two Interval Models for Fuzzy-Valued Genetic Algorithm. In: Suzuki, Y., Hagiya, M. (eds) Recent Advances in Natural Computing. Mathematics for Industry, vol 9. Springer, Tokyo. https://doi.org/10.1007/978-4-431-55105-8_2
Download citation
DOI: https://doi.org/10.1007/978-4-431-55105-8_2
Published:
Publisher Name: Springer, Tokyo
Print ISBN: 978-4-431-55104-1
Online ISBN: 978-4-431-55105-8
eBook Packages: EngineeringEngineering (R0)