Abstract
The cooperative coevolution paradigm decomposes a large problem into a set of subcomponents and solves them independently in order to collectively solve the large problem. This work introduces a novel encoding scheme for building subcomponents based on functional properties of a neuron. The encoding scheme is used for training feedforward neural networks. The results show that the proposed encoding scheme achieves better performance when compared to its previous counterparts.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Potter, M.A., Jong, K.A.D.: A cooperative coevolutionary approach to function optimization. In: PPSN III: Proceedings of the International Conference on Evolutionary Computation. The Third Conference on Parallel Problem Solving from Nature, London, UK, pp. 249–257. Springer, Heidelberg (1994)
Yang, Z., Tang, K., Yao, X.: Large scale evolutionary optimization using cooperative coevolution. Inf. Sci. 178(15), 2985–2999 (2008)
Potter, M.A., De Jong, K.A.: Cooperative coevolution: An architecture for evolving coadapted subcomponents. Evol. Comput. 8(1), 1–29 (2000)
GarcÃa-Pedrajas, N., Ortiz-Boyer, D.: A cooperative constructive method for neural networks for pattern recognition. Pattern Recogn. 40(1), 80–98 (2007)
Garcia-Pedrajas, N., Hervas-Martinez, C., Munoz-Perez, J.: COVNET: a cooperative coevolutionary model for evolving artificial neural networks. IEEE Transactions on Neural Networks 14(3), 575–596 (2003)
Garcia-Pedrajas, N., Hervas-Martinez, C., Munoz-Perez, J.: Multi-objective cooperative coevolution of artificial neural networks (multi-objective cooperative networks). Neural Netw. 15(10), 1259–1278 (2002)
Gomez, F., Mikkulainen, R.: Incremental evolution of complex general behavior. Adapt. Behav. 5(3-4), 317–342 (1997)
Gomez, F.J.: Robust non-linear control through neuroevolution. Technical Report AI-TR-03-303, PhD Thesis, Department of Computer Science, The University of Texas at Austin (2003)
Gomez, F., Schmidhuber, J., Miikkulainen, R.: Accelerated neural evolution through cooperatively coevolved synapses. J. Mach. Learn. Res. 9, 937–965 (2008)
Asuncion, A., Newman, D.: UCI Machine Learning Repository (2007)
Deb, K., Anand, A., Joshi, D.: A computationally efficient evolutionary algorithm for real-parameter optimization. Evol. Comput. 10(4), 371–395 (2002)
CantuPaz, E., Kamath, C.: An empirical comparison of combinations of evolutionary algorithms and neural networks for classification problems. IEEE Transactions on Systems, Man and Cybernetics-Part B: Cybernetics 35(5), 915–933 (2005)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2010 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Chandra, R., Frean, M., Zhang, M. (2010). An Encoding Scheme for Cooperative Coevolutionary Feedforward Neural Networks. In: Li, J. (eds) AI 2010: Advances in Artificial Intelligence. AI 2010. Lecture Notes in Computer Science(), vol 6464. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-17432-2_26
Download citation
DOI: https://doi.org/10.1007/978-3-642-17432-2_26
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-17431-5
Online ISBN: 978-3-642-17432-2
eBook Packages: Computer ScienceComputer Science (R0)