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.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Shen, F., Ouyang, Q., Kasai, W., Hasegawa, O.: A general associative memory based on self-organizing incremental neural network. Neurocomputing 104, 57–71 (2013)
Fišer, D., Faigl, J., Kulich, M.: Growing neural gas efficiently. Neurocomputing 104, 72–82 (2013)
Allahyar, A., Yazdi, H.S., Harati, A.: Constrained semi-supervised growing self-organizing map. Neurocomputing 147, 456–471 (2015)
Reiner, P., Wilamowski, B.M.: Efficient incremental construction of RBF networks using quasi-gradient method. Neurocomputing 150, 349–356 (2015)
Zhang, Y., Ji, H., Zhang, W.: TPPFAM: Use of threshold and posterior probability for category reduction in fuzzy ARTMAP. Neurocomputing 124, 63–71 (2014)
Kirkpatrick, S., Gelatto, C.D., Vecchi, M.P.: Optimization by simulated annealing. Science 220, 671–680 (1983)
Rashedi, E., Nezamabadi-pour, H., Saryazdi, S.: GSA: A gravitational search algorithm. Inf. Sci. 179, 2232–2248 (2009)
Mitchell, M.: An Introduction to Genetic Algorithms. MIT Press, Cambridge (1996)
Farmer, J.D., Packard, N., Perelson, A.: The immune system, adaptation and machine learning. Phys. D 2, 187–204 (1986)
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)
Kennedy, J., Eberhart, R.C.: Particle swarm optimization. Proc. IEEE Int. Conf. Neural Netw. 4, 1942–1948 (1995)
Passino, K.M.: Biomimicry of bacterial foraging for distributed optimization and control. IEEE Trans. Control Syst. Mag. 22, 52–67 (2002)
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)
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)
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)
Baskar, S., Subraraj, P., Rao, M.V.C.: Performance of hybrid real coded genetic algorithms. Int. J. Comput. Eng. Sci. 2, 583–602 (2001)
Ripley, B.D.: Neural networks and related methods for classification. J. Roy. Stat. Soc.: Ser. B (Methodol.) 56, 409–456 (1994)
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)
Efron, B.: Bootstrap methods: another look at the jackknife. Ann. Stat. 7, 1–26 (1979)
Ding, S., Xu, L., Su, C., **, F.: An optimizing method of rbf neural network based on genetic algorithm. Neural Comput. Appl. 21(2012), 333–336 (2012)
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)
Tallón-Ballesteros, A.J., Hervás-MartÃnez, 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)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2015 Springer International Publishing Switzerland
About this paper
Cite this paper
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
Download citation
DOI: https://doi.org/10.1007/978-3-319-19857-6_51
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-19856-9
Online ISBN: 978-3-319-19857-6
eBook Packages: EngineeringEngineering (R0)