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Evolving an Adaptive Artificial Neural Network with a Gravitational Search Algorithm

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Intelligent Decision Technologies (IDT 2017)

Part of the book series: Smart Innovation, Systems and Technologies ((SIST,volume 39))

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Abstract

In this paper, a supervised fuzzy adaptive resonance theory neural network, i.e., Fuzzy ARTMAP (FAM), is integrated with a heuristic Gravitational Search Algorithm (GSA) that is inspired from the laws of Newtonian gravity. The proposed FAM-GSA model combines the unique features of both constituents to perform data classification. The classification performance of FAM-GSA is benchmarked against other state-of-art machine learning classifiers using an artificially generated data set and two real data sets from different domains. Comparatively, the empirical results indicate that FAM-GSA generally is able to achieve a better classification performance with a parsimonious network size, but with the expense of a higher computational load.

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References

  1. Shen, F., Ouyang, Q., Kasai, W., Hasegawa, O.: A general associative memory based on self-organizing incremental neural network. Neurocomputing 104, 57–71 (2013)

    Article  Google Scholar 

  2. Fišer, D., Faigl, J., Kulich, M.: Growing neural gas efficiently. Neurocomputing 104, 72–82 (2013)

    Article  Google Scholar 

  3. Allahyar, A., Yazdi, H.S., Harati, A.: Constrained semi-supervised growing self-organizing map. Neurocomputing 147, 456–471 (2015)

    Article  Google Scholar 

  4. Reiner, P., Wilamowski, B.M.: Efficient incremental construction of RBF networks using quasi-gradient method. Neurocomputing 150, 349–356 (2015)

    Article  Google Scholar 

  5. Zhang, Y., Ji, H., Zhang, W.: TPPFAM: Use of threshold and posterior probability for category reduction in fuzzy ARTMAP. Neurocomputing 124, 63–71 (2014)

    Article  Google Scholar 

  6. Kirkpatrick, S., Gelatto, C.D., Vecchi, M.P.: Optimization by simulated annealing. Science 220, 671–680 (1983)

    Article  MATH  MathSciNet  Google Scholar 

  7. Rashedi, E., Nezamabadi-pour, H., Saryazdi, S.: GSA: A gravitational search algorithm. Inf. Sci. 179, 2232–2248 (2009)

    Article  MATH  Google Scholar 

  8. Mitchell, M.: An Introduction to Genetic Algorithms. MIT Press, Cambridge (1996)

    Google Scholar 

  9. Farmer, J.D., Packard, N., Perelson, A.: The immune system, adaptation and machine learning. Phys. D 2, 187–204 (1986)

    Article  MathSciNet  Google Scholar 

  10. Dorigo, M., Maniezzo, V., Colorni, A.: Ant system: optimization by a colony of cooperating agents. IEEE Trans. Syst. Man, Cybern. Part B 26, 29–41 (1996)

    Article  Google Scholar 

  11. Kennedy, J., Eberhart, R.C.: Particle swarm optimization. Proc. IEEE Int. Conf. Neural Netw. 4, 1942–1948 (1995)

    Google Scholar 

  12. Passino, K.M.: Biomimicry of bacterial foraging for distributed optimization and control. IEEE Trans. Control Syst. Mag. 22, 52–67 (2002)

    Article  Google Scholar 

  13. Karakış, R., Tez, M., Kılıç, Y.A., Kuru, Y., Güler, İ.: A genetic algorithm model based on artificial neural network for prediction of the axillary lymph node status in breast cancer. Eng. Appl. Artif. Intell. 26, 945–950 (2013)

    Article  Google Scholar 

  14. Wong, T.C., Ngan, S.C.: A comparison of hybrid genetic algorithm and hybrid particle swarm optimization to minimize make span for assembly job shop. Appl. Soft Comput. 13, 1391–1399 (2013)

    Article  Google Scholar 

  15. Carpenter, G.A., Grossberg, S., Markuzon, N., Reynolds, J.H., Rosen, D.B.: Fuzzy artmap: a neural network architecture for incremental supervised learning of analog multidimensional maps. IEEE Trans. Neural Networks 3, 698–713 (1992)

    Article  Google Scholar 

  16. Baskar, S., Subraraj, P., Rao, M.V.C.: Performance of hybrid real coded genetic algorithms. Int. J. Comput. Eng. Sci. 2, 583–602 (2001)

    Article  Google Scholar 

  17. Ripley, B.D.: Neural networks and related methods for classification. J. Roy. Stat. Soc.: Ser. B (Methodol.) 56, 409–456 (1994)

    MATH  MathSciNet  Google Scholar 

  18. Asuncion, A., Newman, D.J.: UCI Machine Learning Repository [http://www.ics.uci.edu/~mlearn/MLRepository.html]. University of California, School of Information and Computer Science, Irvine, CA (2007)

  19. Efron, B.: Bootstrap methods: another look at the jackknife. Ann. Stat. 7, 1–26 (1979)

    Article  MATH  MathSciNet  Google Scholar 

  20. Ding, S., Xu, L., Su, C., Jin, F.: An optimizing method of rbf neural network based on genetic algorithm. Neural Comput. Appl. 21(2012), 333–336 (2012)

    Article  Google Scholar 

  21. Kohavi, R.: A study of cross validation and bootstrap for accuracy estimation and model selection. In: Proceedings of the 14th International Joint Conference Artificial Intelligence (IJCAI), pp. 1137–1145. Morgan Kaufmann (1995)

    Google Scholar 

  22. Tallón-Ballesteros, A.J., Hervás-Martínez, C., Riquelme, J.C., Ruiz, R.: Feature selection to enhance a two-stage evolutionary algorithm in product unit neural networks for complex classification problems. Neurocomputing 114, 107–117 (2013)

    Article  Google Scholar 

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Author information

Authors and Affiliations

  1. Multimedia University, Cyberjaya, Malaysia

    Shing Chiang Tan

  2. Deakin University, Geelong, Victoria

    Chee Peng Lim

Authors
  1. Shing Chiang Tan
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  2. Chee Peng Lim
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Correspondence to Shing Chiang Tan .

Editor information

Editors and Affiliations

  1. FCT, Universidade Nova de Lisboa, Caparica, Portugal

    Rui Neves-Silva

  2. Faculty of Education, Science, Technology and Mathematics, University of Canberra, Canberra, Australia

    Lakhmi C. Jain

  3. KES International, Shoreham-by-sea, United Kingdom

    Robert J. Howlett

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Tan, S.C., Lim, C.P. (2015). Evolving an Adaptive Artificial Neural Network with a Gravitational Search Algorithm. In: Neves-Silva, R., Jain, L., Howlett, R. (eds) Intelligent Decision Technologies. IDT 2017. Smart Innovation, Systems and Technologies, vol 39. Springer, Cham. https://doi.org/10.1007/978-3-319-19857-6_51

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  • DOI: https://doi.org/10.1007/978-3-319-19857-6_51

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-19856-9

  • Online ISBN: 978-3-319-19857-6

  • eBook Packages: EngineeringEngineering (R0)

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