Abstract
Evolutionary programming (EP) has been used for the adaptation (optimization) of IIR filters. In a previous study [1], the rate of optimization using EP was shown to be dependent on the structure of the filter used during realization. Furthermore, this dependency changes with the filter order. In this paper, the reasons for such a dependence are investigated. Gradient-based algorithms are also affected by the filter realization, which determines the nature of the mean squared error surface. EP is robust to the presence of local minima and while ensuring the stability of the generated solution offers provable global convergence in the limit. The error surfaces, as seen by EP, while modeling these IIR filters in various realizations, namely, direct, cascade, parallel, and lattice form are analyzed. Experimental results show that ‘gradient friendly’ error surfaces, corresponding to favorable realizations when using gradient based techniques, are not necessarily ‘EP friendly’ and vice versa.
This is a preview of subscription content, log in via an institution to check access.
Preview
Unable to display preview. Download preview PDF.
References
K. Chellapilla, D. B. Fogel, S. S. Rao (1996) “Optimizing IIR Filters using Evolutionary Programming,” Adaptive Distr. Parallel Computing Symposium, pp 252–258, Dayton OH.
Q. Ma, C. F. N. Cowan (1996) “Genetic algorithms applied to the adaptation of IIR filters,” Sigal Processing, vol. 48, pp 155–163.
R. Nambiar and P. Mars (1992) “Genetic and annealing approaches to adaptive digital filtering,” Proc. 26th Asilomar Conf. on Signals, Systems and Computers, IEEE Computer Society Press, Los Altos, CA, pp. 871–875.
K. Steiglitz and L. E. McBride (1965) “A technique for the identification of linear systems,” IEEE Trans. Automat. Control, vol. AC-10, pp. 461–464.
H. Fan and W.K. Jenkins (1986) “A new adaptive IIR filter”, IEEE Trans. Circuits Systems, vol. CAS-33, pp 939–947.
D. Parikh, N. Ahmed and S.D. Stearns (1980) “An adaptive lattice algorithm for recursive filters,” IEEE Trans. Acoust. Speech Signal Processing, vol. ASSP-28, pp. 110–111.
T. A. C. M. Claasen, W. F. G. Mecklenbrauker, and J.B.H. Peek (1979) “Effects of quantization and overflow in recursive digital filters,” IEEE Trans. Acoust. Speech Sig. Proc., vol. ASSP-24, pp. 517–529.
M. Nayeri and W.K. Jenkins (1989) “Alternate realizations to adaptive IIR filters and properties of their performance surfaces,” IEEE Trans. Circuits Syst., vol. 36:4, pp. 485–496.
J. J. Shynk (1987) “Performance of alternate adaptive IIR filter realization,” Proc. of 21st Asilomar Conf. on Signals, Systems and Computers, Maple Press, San Jose, CA.
L. J. Fogel, A. J. Owens and M. J. Walsh (1966) Artificial Intelligence Through Simulated Evolution, New York: John Wiley.
D. B. Fogel, L. J. Fogel, W. Atmar (1991) “Meta-evolutionary programming,” in Proc. of the 25th Asilomar Conf. on Signals, Systems and Computers, R. R. Chen, Ed. IEEE computer Society, pp. 540–545.
P.A. Regalia (1992) “Stable and efficient lattice algorithms for adaptive IIR filtering,” IEEE Trans. Signal Process. vol. 40, pp. 375–388.
Author information
Authors and Affiliations
Editor information
Rights and permissions
Copyright information
© 1997 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Chellapilla, K., Fogel, D.B., Rao, S.S. (1997). Gaining insight into evolutionary programming through landscape visualization: An investigation into IIR filtering. In: Angeline, P.J., Reynolds, R.G., McDonnell, J.R., Eberhart, R. (eds) Evolutionary Programming VI. EP 1997. Lecture Notes in Computer Science, vol 1213. Springer, Berlin, Heidelberg. https://doi.org/10.1007/BFb0014829
Download citation
DOI: https://doi.org/10.1007/BFb0014829
Published:
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-62788-3
Online ISBN: 978-3-540-68518-0
eBook Packages: Springer Book Archive