Skip to main content
SpringerLink
Log in

Direct and Indirect Evolutionary Designs of Artificial Neural Networks

  • Chapter
  • First Online:
Intuitionistic and Type-2 Fuzzy Logic Enhancements in Neural and Optimization Algorithms: Theory and Applications

Part of the book series: Studies in Computational Intelligence ((SCI,volume 862))

  • 675 Accesses

Abstract

Evolutionary Algorithms (EAs) and other kind of metaheuristics are utilized to either design or optimize the architecture of Artificial Neural Networks (ANNs) in order to adapt them for solving a specific problem; these generated ANNs are known as Evolutionary Artificial Neural Networks (EANNs). Their architecture components, including number of neurons or their kind of transfer functions, connectivity pattern, etc., can be defined through direct or indirect encoding schemes; the former, directly codifies ANN architecture components into the genotype of solutions, meanwhile the last one, requires to transform the solution’s genotype through mapping processes to generate an ANN architecture. In this work, architectures of three-layered feed-forward ANNs are designed by using both encoding schemes to solve several well-known benchmark datasets of supervised classification problems. The results of designed ANNs by using direct and indirect encoding schemes are compared.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 189.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 249.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 249.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Amaldi, E., Mayoraz, E., de Werra, D.: A review of combinatorial problems arising in feedforward neural network design. Discrete Appl. Math. 52(2), 111–138 (1994)

    Article  MathSciNet  Google Scholar 

  2. Blum, A.L., Rivest, R.L.: Training a 3-node neural network is NP-complete. Neural Netw. 5(1), 117–127 (1992)

    Article  Google Scholar 

  3. DasGupta, B., Siegelmann, H.T., Sontag, E.: On the intractability of loading neural networks. In: Theoretical Advances in Neural Computation and Learning, pp. 357–389. Springer, Heidelberg (1994)

    Chapter  Google Scholar 

  4. Dempsey, I., O’Neill, M., Brabazon, A.: Foundations In Grammatical Evolution For Dynamic Environments, vol. 194. Springer, Heidelberg (2009)

    Book  Google Scholar 

  5. Dheeru, D., Karra Taniskidou, E.: UCI Machine Learning Repository (2017)

    Google Scholar 

  6. Ding, S., Li, H., Su, C., Yu, J., Jin, F.: Evolutionary artificial neural networks: a review. Artif. Intell. Rev. 39(3), 251–260 (2013)

    Article  Google Scholar 

  7. Elizondo, D., Fiesler, E.: A survey of partially connected neural networks. Int. J. Neural Syst. 8(5–6), 535–558 (1997)

    Article  Google Scholar 

  8. Floreano, D., Dürr, P., Mattiussi, C.: Neuroevolution: from architectures to learning. Evol. Intell. 1(1), 47–62 (2008)

    Article  Google Scholar 

  9. Garro, B.A., Sossa, H., Vazquez, R.A.: Design of artificial neural networks using a modified particle swarm optimization algorithm. In: Proceedings Of The 2009 International Joint Conference On Neural Networks, IJCNN’09, pp. 2363–2370, Piscataway, NJ, USA, IEEE Press (2009)

    Google Scholar 

  10. Garro, B.A., Sossa, H., Vázquez, R.A.: Design of artificial neural networks using differential evolution algorithm. In: International Conference on Neural Information Processing, pp. 201–208. Springer, Heidelberg (2010)

    Google Scholar 

  11. Garro, B.A., Sossa, H., Vázquez, R.A.: Artificial neural network synthesis by means of artificial bee colony (abc) algorithm. In: 2011 IEEE Congress of Evolutionary Computation (CEC), pp. 331–338. IEEE (2011)

    Google Scholar 

  12. Garro, B.A., Sossa, H., Vázquez, R.A.: Evolving neural networks: a comparison between differential evolution and particle swarm optimization. In: International Conference in Swarm Intelligence, pp. 447–454. Springer, Heidelberg (2011)

    Google Scholar 

  13. Garro, B.A., Vázquez, R.A.: Designing artificial neural networks using particle swarm optimization algorithms. Comput. Intell. Neurosci. 2015, 61 (2015)

    Article  Google Scholar 

  14. Hagan, M., Demuth, H., Beale, M.: Neural Network Design. Martin Hagan (2014)

    Google Scholar 

  15. Haykin, S.: Neural Networks: Comprehensive Foundation. Prentice Hall (1999)

    Google Scholar 

  16. Judd, J.S.: On the complexity of loading shallow neural networks. J. Complexity 4(3), 177–192 (1988)

    Article  MathSciNet  Google Scholar 

  17. Judd, J.S.: Neural Network Design and the Complexity of Learning. Neural Network Modeling and Connectionism Series. MIT press, Cambridge (MA) (1990)

    Book  Google Scholar 

  18. Koza, J.R.: Genetic Programming: On the Programming of Computers by Means of Natural Selection. A Bradford Book, vol. 1. MIT press, Cambridge (MA) (1992)

    Google Scholar 

  19. Meduna, A.: Automata and Languages: Theory and Applications. Springer, London (2012)

    MATH  Google Scholar 

  20. Ojha, V.K., Abraham, A., Snášel, V.: Metaheuristic design of feedforward neural networks: a review of two decades of research. Eng. Appl. Artif. Intell. 60, 97–116 (2017)

    Article  Google Scholar 

  21. Quiroz-Ramírez, O., Espinal, A., Ornelas-Rodríguez, M., Rojas-Domínguez, A., Sánchez, D., Puga-Soberanes, H., Carpio, M., Espinoza, L.E.M., Ortíz-López, J.: Partially-connected artificial neural networks developed by grammatical evolution for pattern recognition problems. Stud. Comput. Intell. 749, 99–112 (2018)

    Google Scholar 

  22. Ryan, C., Collins, J., O’Neill, M.: Grammatical evolution: evolving programs for an arbitrary language. Genetic Programming: First European Workshop. EuroGP’98 Paris, France, 14–15 April 1998 Proceedings, pp. 83–96. Springer, Heidelberg (1998)

    Google Scholar 

  23. Storn, R., Price, K.: Differential evolution-a simple and efficient heuristic for global optimization over continuous spaces. J. Glob. Optimization 11(4), 341–359 (1997)

    Article  MathSciNet  Google Scholar 

  24. Tayefeh, M., Taghiyareh, F., Forouzideh, N., Caro, L.: Evolving artificial neural network structure using grammar encoding and colonial competitive algorithm. Neural Comput. Appl. 22(1), 1–16 (2013)

    Article  Google Scholar 

  25. Veerarajan, T.: Discrete Mathematics. McGraw-Hill Education (India) Pvt Limited (2006)

    Google Scholar 

  26. Yao, X.: Evolving artificial neural networks. Proc. IEEE 87(9), 1423–1447 (1999)

    Article  Google Scholar 

  27. Zhang, G., Patuwo, B.E., Hu, M.Y.: Forecasting with artificial neural networks: the state of the art. Int. J. Forecast. 14(1), 35–62 (1998)

    Article  Google Scholar 

  28. Zhang, G.P.: Neural networks for classification: a survey. IEEE Trans. Syst. Man Cybern. Part C (Applications and Reviews) 30(4), 451–462 (2000)

    Article  MathSciNet  Google Scholar 

Download references

Acknowledgements

Authors wish to thank Universidad de Guanajuato, Tecnológico Nacional de México y Universidad de la Salle. This work was supported by the CONACyT Project FC2016-1961 "Neurociencia Computacional: de la teoría al desarrollo de sistemas neuromórficos".

Author information

Authors and Affiliations

  1. Departamento de Estudios Organizacionales, DCEA-Universidad de Guanajuato, Guanajuato, Mexico

    O. Alba-Cisneros, A. Espinal, M. A. Sotelo-Figueroa & O. J. Purata-Sifuentes

  2. División de Estudios de Posgrado e Investigación, Tecnológico Nacional de México / Instituto Tecnológico de León, León, Guanajuato, Mexico

    G. López-Vázquez

  3. Tecnológico Nacional de México / Instituto Tecnológico Superior de Purísima del Rincón. Purísima del Rincón, Guanajuato, Mexico

    V. Calzada-Ledesma

  4. Grupo de Sistemas Inteligentes, Facultad de Ingeniería, Universidad de La Salle, Delegación Cuauhtémoc, Ciudad de México, Mexico

    R. A. Vázquez

  5. Departamento de Electrónica, DICIS-Universidad de Guanajuato, Salamanca, Guanajuato, Mexico

    H. Rostro-González

Authors
  1. O. Alba-Cisneros
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. A. Espinal
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. G. López-Vázquez
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. M. A. Sotelo-Figueroa
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. O. J. Purata-Sifuentes
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. V. Calzada-Ledesma
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. R. A. Vázquez
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. H. Rostro-González
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to A. Espinal .

Editor information

Editors and Affiliations

  1. Division of Graduate Studies and Research, Tijuana Institute of Technology, Tijuana, Baja California, Mexico

    Oscar Castillo

  2. Division of Graduate Studies and Research, Tijuana Institute of Technology, Tijuana, Baja California, Mexico

    Patricia Melin

  3. Systems Research Institute, Polish Academy of Sciences, Warsaw, Poland

    Janusz Kacprzyk

Rights and permissions

Reprints and permissions

Copyright information

© 2020 Springer Nature Switzerland AG

About this chapter

Check for updates. Verify currency and authenticity via CrossMark

Cite this chapter

Alba-Cisneros, O. et al. (2020). Direct and Indirect Evolutionary Designs of Artificial Neural Networks. In: Castillo, O., Melin, P., Kacprzyk, J. (eds) Intuitionistic and Type-2 Fuzzy Logic Enhancements in Neural and Optimization Algorithms: Theory and Applications. Studies in Computational Intelligence, vol 862. Springer, Cham. https://doi.org/10.1007/978-3-030-35445-9_31

Download citation

Publish with us

Policies and ethics

Search

Navigation