Abstract
Different credit assignment strategies are investigated in a two level co-evolutionary model which involves a population of Gaussian neurons and a population of radial basis function networks consisting of neurons from the neuron population. Each individual in neuron population can contribute to one or more networks in network population, so there is a two-fold difficulty in evaluating the effectiveness (or fitness) of a neuron. Firstly, since each neuron only represents a partial solution to the problem, it needs to be assigned some credit for the complete problem solving activity. Secondly, these credits need to be accumulated from different networks the neuron participates in. This model, along with various credit assignment strategies, is tested on a classification (Heart disease diagnosis problem from UCI machine learning repository) and a regression problem (Mackey-Glass time series prediction problem).
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
Similar content being viewed by others
References
Potter, M.A., Jong, K.A.D.: Cooperative Coevolution: An Architecture for Evolving Coadapted Subcomponents. Evolutionary Computation 8, 1–29 (2000)
Yong, C.H., Miikkulainen, R.: Cooperative Coevolution of Multi-Agent Systems. Technical Report AI01-287, Department of computer Sciences, The University of Texas at Austin, Austin, TX 78712 USA (2001)
Smalz, R., Conrad, M.: Combining Evolution With Credit Apportionment: A New Learning Algorithm for Neural Nets. Neural Networks 7, 341–351 (1994)
Moriarty, D.E., Miikkulainen, R.: Forming Neural Networks Through Efficient and Adaptive Coevolution. Evolutionary Computation 5, 373–399 (1997)
Igel, C., Hüsken, M.: Empirical Evaluation of the Improved Rprop Learning Algorithm. Neurocomputing 50, 105–123 (2003)
Riedmiller, M., Braun, H.: A Direct Adaptive Method for Faster Backpropagation Learning: The RPROP algorithm. In: Proceedings of the IEEE International Conference on Neural Networks, San Francisco, CA, pp. 586–591 (1993)
Whitehead, B.A., Choate, T.D.: Cooperative-Competitive Genetic Evolution of Radial Basis Function Centers and Widths for Time Series Prediction. IEEE Transactions on Neural Networks 7, 869–880 (1996)
Whitehead, B.A.: Genetic Evolution of Radial Basis Function Coverage Using Orthogonal Niches. IEEE Transactions on Neural Networks 7, 1525–1528 (1996)
Hüsken, M., Gayko, J.E., Sendhoff, B.: Optimization for Problem Classes - Neural Networks that Learn to Learn. In: Yao, X. (ed.) IEEE Symposium on Combinations of Evolutionary Computation and Neural Networks, pp. 98–109. IEEE Press, Los Alamitos (2000)
Blake, C., Merz, C.: UCI Repository of machine learning databases (1998), http://www.ics.uci.edu/~mlearn/MLRepository.html
Khare, V., Yao, X.: Artificial Speciation and Automatic Modularisation. In: Wang, L., Tan, K.C., Furuhashi, T., Kim, J.H., Yao, X. (eds.) Proceedings of the 4th Asia- Pacific Conference on Simulated Evolution And Learning (SEAL 2002), Singapore, vol. 1, pp. 56–60 (2002)
Yao, X., Liu, Y.: Making Use of Population Information in Evolutionary Artificial Neural Networks. IEEE Transactions on Systems, Man and Cybernetics, Part B: Cybernetics 28, 417–425 (1998)
Yao, X., Liu, Y.: A New Evolutionary System for Evolving Artificial Neural Networks. IEEE Transactions on Neural Networks 8, 694–713 (1997)
Farmer, J.D., Sidorowich, J.J.: Predicting chaotic time series. Physical Review Letters 59, 845–848 (1987)
Mackey, M.C., Glass, L.: Oscillation and chaos in physiological control systems. Science 197, 287–289 (1977)
Martinetz, T.M., Berkovich, S.G., Schulten, K.J.: ‘Neural-Gas’ Network for Vector Quantization and its Application to Time-Series Prediction. IEEE Transactions on Neural Networks 4, 558–569 (1993)
Bishop, C.M.: Neural Networks for Pattern Recogntion. Oxford University Press, Oxford (1995)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2004 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Khare, V.R., Yao, X., Sendhoff, B. (2004). Credit Assignment Among Neurons in Co-evolving Populations. In: Yao, X., et al. Parallel Problem Solving from Nature - PPSN VIII. PPSN 2004. Lecture Notes in Computer Science, vol 3242. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-30217-9_89
Download citation
DOI: https://doi.org/10.1007/978-3-540-30217-9_89
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-23092-2
Online ISBN: 978-3-540-30217-9
eBook Packages: Springer Book Archive