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Artificial Bee Colony (ABC) Optimization Algorithm for Training Feed-Forward Neural Networks

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Modeling Decisions for Artificial Intelligence (MDAI 2007)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 4617))

Abstract

Training an artificial neural network is an optimization task since it is desired to find optimal weight set of a neural network in training process. Traditional training algorithms has some drawbacks such as getting stuck in local minima and computational complexity. Therefore, evolutionary algorithms are employed to train neural networks to overcome these issues. In this work, Artificial Bee Colony (ABC) Algorithm which has good exploration and exploitation capabilities in searching optimal weight set is used in training neural networks.

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References

  1. Rumelhart, D.E., Williams, R.J., Hinton, G.E.: Learning internal representations by error propagation. Parallel Distributed Processing: Explorations in the Microstructure of Cognition 1, 318–362 (1986)

    Google Scholar 

  2. Liu, Z., Liu, A., Wang, C., Niu, Z.: Evolving neural network using real coded genetic algorithm (GA) for multispectral image classification. Future Generation Computer Systems 20, 1119–1129 (2004)

    Article  Google Scholar 

  3. Craven, M.P.: A Faster Learning Neural Network Classifier Using Selective Backpropagation. In: Proceedings of the Fourth IEEE International Conference on Electronics, Circuits and Systems, Cairo, Egypt, December 15-18, 1997, vol. 1, pp. 254–258 (1997)

    Google Scholar 

  4. Whitley, D., Starkweather, T., Bogart, C.: Genetic algorithms and neural networks: Optimizing connections and connectivity. Techn. Rep. CS 89-117, Department of Computer Science, Colorado State University (1989)

    Google Scholar 

  5. Rudnick, M.: A bibliography of the intersection of genetic search and artificial neural networks. Techn. Rep. No. CS/E 90- 001. Oregon Graduate Center, Beaverton, OR (1990)

    Google Scholar 

  6. Weiß, G.: Combining neural and evolutionary learning: Aspects and approaches. Techn. Rep. FKI-132-90. Institut fur Informatik, Technische Universit at Munchen (1990)

    Google Scholar 

  7. Weiß, G.: Towards the synthesis of neural and evolutionary learning. In: Omidvar, O. (ed.) Progress in neural networks, ch. 5, vol. 5, Ablex, Norwood, NJ (1993)

    Google Scholar 

  8. Albrecht, R.F., Reeves, C.R., Steele, N.C.: Artificial neural nets and genetic algorithms. Proceedings of the International Conference, Innsbruck, Austria. Springer, Heidelberg (1993)

    MATH  Google Scholar 

  9. Jones, A.J.: Genetic algorithms and their applications to the design of neural networks. Neural Computing & Applications 1, 32–45 (1993)

    Article  Google Scholar 

  10. Ling, S.H., Lam, H.K., Leung, F.H.F., Lee, Y.S.: A Genetic Algorithm Based Variable Structure Neural Network, Industrial Electronics Society, 2003. In: IECON 2003. The 29th Annual Conference of the IEEE, vol. 1, pp. 436–441 (2003)

    Google Scholar 

  11. Marshall, S.J., Harrison, R.F.: Optimization and Training of Feedforward Neural Networks By Genetic Algorithms, Artificial Neural Networks, 1991. In: Second International Conference on, November 18-20, 1991, pp. 39–43 (1991)

    Google Scholar 

  12. Verma, B., Ghosh, R.: A novel evolutionary neural learning algorithm, Evolutionary Computation, 2002. In: CEC 2002. Proceedings of the 2002 Congress on, vol. 2, pp. 1884–1889 (2002)

    Google Scholar 

  13. Gao, Q., Lei, K.Q.Y., He, Z.: An Improved Genetic Algorithm and Its Application in Artificial Neural Network, Information, Communications and Signal Processing, 2005. In: Fifth International Conference on, December 06-09, 2005, pp. 357–360 (2005)

    Google Scholar 

  14. Yao, X.: Evolvutionary artificial neural networks. International Journal of Neural Systems 4(3), 203–222 (1993)

    Article  Google Scholar 

  15. Eberhart, R.C., Dobbins, R.W.: Designing Neural Network explanation facilities using Genetic algorithms. In: IEEE International Joint Conference on Publication, November 18-21,1991, vol. 2, pp. 1758–1763 (1991)

    Google Scholar 

  16. Jelodar, M.S., Fakhraie, S.M., Ahmadabadi, M.N.: A New Approach for Training of Artificial Neural Networks using Population Based Incremental Learning (PBIL). In: International Conference on Computational Intelligence, pp. 165–168 (2004)

    Google Scholar 

  17. Tsai, J.T., Chou, J.H., Liu, T.K.: Tuning the Structure and Parameters of a Neural Network by Using Hybrid Taguchi-Genetic Algorithm. IEEE Transactions on Neural Networks 17(1) (2006)

    Google Scholar 

  18. El-Gallad, A.I., El-Hawary, M., Sallam, A.A., Kalas, A.: Swarm-intelligently trained neural network for power transformer protection. In: Canadian Conference on Electrical and Computer Engineering, vol. 1, pp. 265–269 (2001)

    Google Scholar 

  19. Mendes, R., Cortez, P., Rocha, M., Neves, J.: Particle swarm for feedforward neural network training. In: Proceedings of the International Joint Conference on Neural Networks, vol. 2, pp. 1895–1899 (2002)

    Google Scholar 

  20. Van der Bergh, F., Engelbrecht, A.: Cooperative learning in neural networks using particle swarm optimizers. South African Computer Journal 26, 84–90 (2000)

    Google Scholar 

  21. Ismail, A., Engelbrecht, A.: Global optimization algorithms for training product unit neural networks. In: IJCNN 2000. International Joint Conference on Neural Networks, vol. 1, pp. 132–137. IEEE Computer Society, Los Alamitos, CA (2000)

    Google Scholar 

  22. Kennedy, J., Eberhart, R.: Swarm Intellegence. Morgan Kaufmann Publishers, San Francisco (2001)

    Google Scholar 

  23. Meissner, M., Schmuker, M., Schneider, G.: Optimized Particle Swarm Optimization (OPSO) and its application to artificial neural network training. BMC Bioinformatics 7, 125 (2006)

    Article  Google Scholar 

  24. Fan, H., Lampinen, J.: A Trigonometric Mutation Operation to Differential Evolution. Journal of Global Optimization 27, 105–129 (2003)

    Article  MATH  MathSciNet  Google Scholar 

  25. Ilonen, J., Kamarainen, J.I., Lampinen, J.: Differential Evolution Training Algorithm for Feed-Forward Neural Networks

    Google Scholar 

  26. Pavlidis, N.G., Tasoulis, D.K., Plagianakos, V.P., Nikiforidis, G., Vrahatis, M.N.: Spiking Neural Network Training Using Evolutionary Algorithms, Neural Networks, 2005. In: IJCNN 2005. Proceedings 2005 IEEE International Joint Conference, vol. 4, pp. 2190–2194 (2005)

    Google Scholar 

  27. Plagianakos, V.P., Vrahatis, M.N.: Training Neural Networks with Threshold Activation Functions and Constrained Integer Weights, Neural Networks, 2000. In: IJCNN 2000. Proceedings of the IEEE-INNS-ENNS International Joint Conference, vol. 5, pp. 161–166 (2000)

    Google Scholar 

  28. Plagianakos, V.P., Vrahatis, M.N.: Neural Network Training with Constrained Integer Weights, Evolutionary Computation, 1999. In: CEC 1999. Proceedings of the 1999 Congress, vol. 3, p. 2013 (1999)

    Google Scholar 

  29. Yu, B., He, X.: Training Radial Basis Function Networks with Differential Evolution, Granular Computing, 2006. In: IEEE International Conference on, May 10-12, 2006, pp. 369–372 (2006)

    Google Scholar 

  30. Yao, X., Liu, Y.: A New Evolutionary System for Evolving Artificial Neural Networks. IEEE Transactions on Neural Networks 8(3), 694–713 (1997)

    Article  MathSciNet  Google Scholar 

  31. Gao, W.: New Evolutionary Neural Networks, 2005. In: First International Conference on Neural Interface and Control Proceedings, May 26-28, 2005, Wuhan, China (2005)

    Google Scholar 

  32. Davoian, K., Lippe, W.: A New Self-Adaptive EP Approach for ANN Weights Training, Enformatika Transactions on Engineering, Computing and Technology, Barcelona, Spain, October 22-24, 2006, vol. 15, pp. 109–114 (2006)

    Google Scholar 

  33. Leung, C., Member, Chow, W.S.: A Hybrid Global Learning Algorithm Based on Global Search and Least Squares Techniques for Backpropagation Networks, Neural Networks,1997. In: International Conference on, vol. 3, pp. 1890–1895 (1997)

    Google Scholar 

  34. Karaboga, D.: An Idea Based On Honey Bee Swarm For Numerical Optimization, Technical Report-TR06, Erciyes University, Engineering Faculty, Computer Engineering Department (2005)

    Google Scholar 

  35. Basturk, B., Karaboga, D.: An Artificial Bee Colony (ABC) Algorithm for Numeric function Optimization. In: IEEE Swarm Intelligence Symposium, May 12-14, 2006, Indianapolis, Indiana, USA (2006)

    Google Scholar 

  36. Weiß, G.: Neural Networks and Evolutionary Computation. PartI: Hybrid Approaches in Artificial Intelligence. In: International Conference on Evolutionary Computation, pp. 268–272 (1994)

    Google Scholar 

  37. Fahlman, S.: An empirical study of learning speed in back-propagation networks, Technical Report CMU-CS-88-162, Carnegie Mellon University, Pittsburgh, PA 15213 (September 1988)

    Google Scholar 

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Authors and Affiliations

  1. Erciyes University, Engineering Faculty, Department of Computer Engineering,  

    Dervis Karaboga, Bahriye Akay & Celal Ozturk

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  1. Dervis Karaboga
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  2. Bahriye Akay
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  3. Celal Ozturk
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Editor information

Vicenç Torra Yasuo Narukawa Yuji Yoshida

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© 2007 Springer-Verlag Berlin Heidelberg

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Karaboga, D., Akay, B., Ozturk, C. (2007). Artificial Bee Colony (ABC) Optimization Algorithm for Training Feed-Forward Neural Networks. In: Torra, V., Narukawa, Y., Yoshida, Y. (eds) Modeling Decisions for Artificial Intelligence. MDAI 2007. Lecture Notes in Computer Science(), vol 4617. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-73729-2_30

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  • DOI: https://doi.org/10.1007/978-3-540-73729-2_30

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-73728-5

  • Online ISBN: 978-3-540-73729-2

  • eBook Packages: Computer ScienceComputer Science (R0)

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