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Inspired by Nature pp 213–233Cite as

Breaking the Stereotypical Dogma of Artificial Neural Networks with Cartesian Genetic Programming

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Part of the book series: Emergence, Complexity and Computation ((ECC,volume 28))

Abstract

This chapter presents the work done in the field of Cartesian Genetic Programming evolved Artificial Neural Networks (CGPANN). Three types of CGPANN are presented, the Feed-forward CGPANN (FFCGPAN), Recurrent CGPANN and the CGPANN that has developmental plasticity, also called Plastic CGPANN or PCGPANN. Each of these networks is explained with the help of diagrams. Performance results obtained for a number of benchmark problems using these networks are illustrated with the help of tables. Artificial Neural Networks (ANNs) suffer from the dilemma of how to select complexity of the network for a specific task, what should be the pattern of inter-connectivity, and in case of feedback, what topology will produce the best possible results. Cartesian Genetic Programming (CGP) offers the ability to select not only the desired network complexity but also the inter-connectivity patterns, topology of feedback systems, and above all, decides which input parameters should be weighted more or less and which one to be neglected. In this chapter we discuss how CGP is used to evolve the architecture of Neural Networks for optimum network and characteristics. Don’t you want a system that designs everything for you? That helps you select the optimal network, the inter-connectivity, the topology, the complexity, input parameters selection and input sensitivity? If yes, then CGP evolved Artificial Neural Network (CGPANN) and CGP evolved Recurrent Neural Network (CGPRNN) is the answer to your questions.

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References

  1. Ali, J., Khan, G.M., Mahmud, S.A.: Enhancing growth curve approach using CGPANN for predicting the sustainability of new food products. In: IFIP International Conference on Artificial Intelligence Applications and Innovations, pp. 286–297. Springer (2014)

    Google Scholar 

  2. Arbab, M.A., Khan, G.M., Sahibzada, A.M.: Cardiac arrhythmia classification using cartesian genetic programming evolved artificial neural network. Exp. Clin. Cardiol. 20(9) (2014)

    Google Scholar 

  3. Bidlo, M.: Evolutionary design of generic combinational multipliers using development. In: International Conference on Evolvable Systems, pp. 77–88. Springer (2007)

    Google Scholar 

  4. Carpenter, G.A., Grossberg, S.: The art of adaptive pattern recognition by a self-organizing neural network. Computer 21(3), 77–88 (1988)

    Article  Google Scholar 

  5. Fekiač, J., Zelinka, I., Burguillo, J.C.: A review of methods for encoding neural network topologies in evolutionary computation. In: Proceedings of 25th European Conference on Modeling and Simulation ECMS 2011, pp. 410–416 (2011)

    Google Scholar 

  6. Gomez, F., Schmidhuber, J., Miikkulainen, R.: Accelerated neural evolution through cooperatively coevolved synapses. J. Mach. Learn. Res. 9(May), 937–965 (2008)

    MATH  MathSciNet  Google Scholar 

  7. Gomez, F.J., Miikkulainen, R.: Solving non-markovian control tasks with neuroevolution. IJCAI 99, 1356–1361 (1999)

    Google Scholar 

  8. Guo, H., Nandi, A.K.: Breast cancer diagnosis using genetic programming generated feature. Pattern Recognit. 39(5), 980–987 (2006)

    Article  Google Scholar 

  9. Haider, A., Hanif, M.N.: Inflation forecasting in Pakistan using artificial neural networks. Pak. Econ. Soc. Rev. 123–138 (2009)

    Google Scholar 

  10. Iranpour, M., Almassi, S., Analoui, M.: Breast cancer detection from fna using svm and rbf classifier. In: 1st Joint Congress on Fuzzy and Intelligent Systems (2007)

    Google Scholar 

  11. Jordan, M.I.: Attractor dynamics and parallellism in a connectionist sequential machine. In: Proceedings of the 8th Confererence of the Cognitive Science Society, pp. 531–546. Lawrence Erlbaum Associates (1986)

    Google Scholar 

  12. Kamruzzaman, J., Sarker, R.A.: Forecasting of currency exchange rates using ANN: a case study. In: Neural Networks and Signal Processing, 2003. Proceedings of the 2003 International Conference on, vol. 1, pp. 793–797. IEEE (2003, December)

    Google Scholar 

  13. Khan, G.M., Ali, J., Mahmud, S.A.: Wind power forecastingan application of machine learning in renewable energy. In: 2014 International Joint Conference on Neural Networks (IJCNN), pp. 1130–1137. IEEE (2014)

    Google Scholar 

  14. Khan, G.M., Khattak, A.R., Zafari, F., Mahmud, S.A.: Electrical load forecasting using fast learning recurrent neural networks. In: The 2013 International Joint Conference on Neural Networks (IJCNN), pp. 1–6. IEEE (2013)

    Google Scholar 

  15. Khan, G.M., Nayab, D., Mahmud, S.A., Zafar, H.: Evolving dynamic forecasting model for foreign currency exchange rates using plastic neural networks. In: 2013 12th International Conference on Machine Learning and Applications (ICMLA), vol. 2, pp. 15–20. IEEE (2013)

    Google Scholar 

  16. Khan, G.M., Ullah, F., Mahmud, S.A.: MPEG-4 internet traffic estimation using recurrent CGPANN. In: International Conference on Engineering Applications of Neural Networks, pp. 22–31. Springer (2013)

    Google Scholar 

  17. Khan, G.M., Zafari, F.: Dynamic feedback neuro-evolutionary networks for forecasting the highly fluctuating electrical loads. Genet. Program. Evolvable Mach. 17(4), 391–408 (2016)

    Article  Google Scholar 

  18. Khan, G.M., Zafari, F., Mahmud, S.A.: Very short term load forecasting using cartesian genetic programming evolved recurrent neural networks (CGPRNN). In: 2013 12th International Conference on Machine Learning and Applications (ICMLA), vol. 2, pp. 152–155. IEEE (2013)

    Google Scholar 

  19. Khan, M.M., Ahmad, A.M., Khan, G.M., Miller, J.F.: Fast learning neural networks using cartesian genetic programming. Neurocomputing 121, 274–289 (2013)

    Article  Google Scholar 

  20. Khan, M.M., Khan, G.M., Miller, J.F.: Developmental plasticity in cartesian genetic programming artificial neural networks. In: Proceedings of the 8th International Conference on Informatics in Control, Automation and Robotics (ICINCO 2011), pp. 449–458. SciTePress (2011)

    Google Scholar 

  21. Kryuchin, O.V., Arzamastsev, A.A., Troitzsch, K.G.: The prediction of currency exchange rates using artificial neural networks. Exch. Organ. Behav. Teach. J. 4 (2007)

    Google Scholar 

  22. Liu, K., Subbarayan, S., Shoults, R., Manry, M., Kwan, C., Lewis, F., Naccarino, J.: Comparison of very short-term load forecasting techniques. IEEE Trans. Power Syst. 11(2), 877–882 (1996)

    Article  Google Scholar 

  23. Moriarty, D.E.: Symbiotic evolution of neural networks in sequential decision tasks. Ph.D. thesis, University of Texas at Austin USA (1997)

    Google Scholar 

  24. Nayab, D., Khan, G.M., Mahmud, S.A.: Prediction of foreign currency exchange rates using cgpann. In: International Conference on Engineering Applications of Neural Networks, pp. 91–101. Springer (2013)

    Google Scholar 

  25. Pan, Z., Nie, L.: Evolving both the topology and weights of neural networks. Parallel Algorithms Appl. 9(3–4), 299–307 (1996)

    Article  MATH  Google Scholar 

  26. Papadrakakis, M., Papadopoulos, V., Lagaros, N.D.: Structural reliability analyis of elastic-plastic structures using neural networks and monte carlo simulation. Comput. Methods Appl. Mech. Eng. 136(1–2), 145–163 (1996)

    Article  MATH  Google Scholar 

  27. Philip, A.A., Taofiki, A.A., Bidemi, A.A.: Artificial neural network model for forecasting foreign exchange rate. World Comput. Sci. Inf. Technol. J. (WCSIT) 1 (3) 110–118 (2011)

    Google Scholar 

  28. Pujol, J.C.F., Poli, R.: Evolving the topology and the weights of neural networks using a dual representation. Appl. Intell. 8(1), 73–84 (1998)

    Article  Google Scholar 

  29. Refenes, A.N., Azema-Barac, M., Chen, L., Karoussos, S.: Currency exchange rate prediction and neural network design strategies. Neural Comput. Appl. 1(1), 46–58 (1993)

    Article  Google Scholar 

  30. Rehman, M., Ali, J., Khan, G.M., Mahmud, S.A.: Extracting trends ensembles in solar irradiance for green energy generation using neuro-evolution. In: IFIP International Conference on Artificial Intelligence Applications and Innovations, pp. 456–465. Springer (2014)

    Google Scholar 

  31. Rehman, M., Khan, G.M., Mahmud, S.A.: Foreign currency exchange rates prediction using cgp and recurrent neural network. IERI Procedia 10, 239–244 (2014)

    Article  Google Scholar 

  32. Sadeghi, B.: A bp-neural network predictor model for plastic injection molding process. J. Mater. Process. Technol. 103(3), 411–416 (2000)

    Article  MathSciNet  Google Scholar 

  33. Sarangi, P.P., Sahu, A., Panda, M.: A hybrid differential evolution and back-propagation algorithm for feedforward neural network training. Int. J. Comput. Appl. 84(14) (2013)

    Google Scholar 

  34. Singh, D., Singh, S.: A self-selecting neural network for short-term load forecasting. Electr. Power Compon. Syst. 29(2), 117–130 (2001)

    Article  Google Scholar 

  35. Stanley, K.O., Miikkulainen, R.: Efficient reinforcement learning through evolving neural network topologies. In: Proceedings of the 4th Annual Conference on Genetic and Evolutionary Computation, pp. 569–577. Morgan Kaufmann Publishers Inc. (2002)

    Google Scholar 

  36. Wieland, A.P.: Evolving neural network controllers for unstable systems. In: IJCNN-91-Seattle International Joint Conference on Neural Networks, 1991, vol. 2, pp. 667–673. IEEE (1991)

    Google Scholar 

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Author information

Authors and Affiliations

  1. Department of Electrical Engineering, University of Engineering and Technology, Peshawar, Pakistan

    Gul Muhammad Khan

  2. Department of Computer Systems Engineering, University of Engineering and Technology, Peshawar, Pakistan

    Arbab Masood Ahmad

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  1. Gul Muhammad Khan
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  2. Arbab Masood Ahmad
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Correspondence to Gul Muhammad Khan .

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Editors and Affiliations

  1. Department of Computer Science, University of York, York, United Kingdom

    Susan Stepney

  2. Unconventional Computing Centre, University of the West of England, Bristol, United Kingdom

    Andrew Adamatzky

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Khan, G.M., Ahmad, A.M. (2018). Breaking the Stereotypical Dogma of Artificial Neural Networks with Cartesian Genetic Programming. In: Stepney, S., Adamatzky, A. (eds) Inspired by Nature. Emergence, Complexity and Computation, vol 28. Springer, Cham. https://doi.org/10.1007/978-3-319-67997-6_10

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  • DOI: https://doi.org/10.1007/978-3-319-67997-6_10

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  • Publisher Name: Springer, Cham

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  • Online ISBN: 978-3-319-67997-6

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