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2018 | OriginalPaper | Chapter

Evolutionary Design and Training of Artificial Neural Networks

Authors : Lumír Kojecký, Ivan Zelinka

Published in: Artificial Intelligence and Soft Computing

Publisher: Springer International Publishing

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Abstract

The dynamics of neural networks and evolutionary algorithms share common attributes and based on many research papers it seems to be that from dynamic point of view are both systems indistinguishable. In order to compare them mutually from this point of view, artificial neural networks, as similar as possible to natural one, are needed. In this paper is described part of our research that is focused on the synthesis of artificial neural networks. Since most current ANN structures are not common in nature, we introduce a method of a complex network synthesis using network growth model, considered as a neural network. Synaptic weights of the synthesized ANN are then trained by an evolutionary algorithm to respond to an input training set successfully.

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previous chapter New Running Technique for the Bison Algorithm

next chapter Obtaining Pareto Front in Instance Selection with Ensembles and Populations

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Beyer, H.G., Schwefel, H.P.: Evolution strategies-a comprehensive introduction. Nat. Comput. 1(1), 3–52 (2002)MathSciNetCrossRef

Bianconi, G., Darst, R.K., Iacovacci, J., Fortunato, S.: Triadic closure as a basic generating mechanism of communities in complex networks. Phys. Rev. E 90(4), 042806 (2014)CrossRef

Hornik, K., Stinchcombe, M., White, H.: Multilayer feedforward networks are universal approximators. Neural Netw. 2(5), 359–366 (1989)CrossRef

Kojecký, L., Zelinka, I., Prasad, A., Vantuch, T., Tomaszek, L.: Investigation on unconventional synthesis of astroinformatic data classificator powered by irregular dynamics. IEEE Intell. Syst. (in press)

Kojeckỳ, L., Zelinka, I., Šaloun, P.: Evolutionary synthesis of automatic classification on astroinformatic big data. Int. J. Parallel Emerg. Distrib. Syst. 32(5), 429–447 (2017)CrossRef

LeCun, Y., Bengio, Y., Hinton, G.: Deep learning. Nature 521(7553), 436–444 (2015)CrossRef

Leung, F.H.F., Lam, H.K., Ling, S.H., Tam, P.K.S.: Tuning of the structure and parameters of a neural network using an improved genetic algorithm. IEEE Trans. Neural Netw. 14(1), 79–88 (2003)CrossRef

Matsumoto, M., Nishimura, T.: Mersenne Twister: a 623-dimensionally equidistributed uniform pseudo-random number generator. ACM Trans. Model. Comput. Simul. (TOMACS) 8(1), 3–30 (1998)CrossRef

Onwubolu, G.C., Babu, B.: New Optimization Techniques in Engineering, vol. 141. Springer, Heidelberg (2004). https://doi.org/10.1007/978-3-540-39930-8CrossRefMATH

10.

Oplatková, Z.: Metaevolution-synthesis of evolutionary algorithms by means of symbolic regression. Tomas Bata University in Zlín (2008)

11.

Vařacha, P.: Neural network synthesis (2011)

12.

Williams, R.J., Zipser, D.: A learning algorithm for continually running fully recurrent neural networks. Neural Comput. 1(2), 270–280 (1989)CrossRef

13.

Zelinka, I.: Analytic programming by means of soma algorithm. In: Proceedings of the 8th International Conference on Soft Computing, Mendel, vol. 2, pp. 93–101 (2002)

Title: Evolutionary Design and Training of Artificial Neural Networks
Authors: Lumír Kojecký
Ivan Zelinka
Publisher: Springer International Publishing
Book: Artificial Intelligence and Soft Computing
Print ISBN: 978-3-319-91252-3

Electronic ISBN: 978-3-319-91253-0

Copyright Year: 2018
DOI: https://doi.org/10.1007/978-3-319-91253-0_40

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