Call for Paper

CAE solicits original research papers for the July 2021 Edition. Last date of manuscript submission is June 30, 2021.

Read More

Design of Digital Low Pass IIR Filter using Real Coded Genetic Algorithm

Bhagyashree K. Jagtap, Mahesh S. Chavan. Published in Algorithms.

Communications on Applied Electronics
Year of Publication: 2016
Publisher: Foundation of Computer Science (FCS), NY, USA
Authors: Bhagyashree K. Jagtap, Mahesh S. Chavan

Bhagyashree K Jagtap and Mahesh S Chavan. Article: Design of Digital Low Pass IIR Filter using Real Coded Genetic Algorithm. Communications on Applied Electronics 4(8):1-4, April 2016. Published by Foundation of Computer Science (FCS), NY, USA. BibTeX

	author = {Bhagyashree K. Jagtap and Mahesh S. Chavan},
	title = {Article: Design of Digital Low Pass IIR Filter using Real Coded Genetic Algorithm},
	journal = {Communications on Applied Electronics},
	year = {2016},
	volume = {4},
	number = {8},
	pages = {1-4},
	month = {April},
	note = {Published by Foundation of Computer Science (FCS), NY, USA}


This paper presents the design method for Infinite Impulse Response (IIR) digital filter with the use of Real Coded Genetic Algorithm (RGA/RCGA). Generally, nonlinear and multimodal error surface is observed in case of digital IIR filter. Hence, to avoid problems such as local minima, global optimization techniques are required. This paper gives effective method to design digital IIR filters. This paper finds the optimum coefficients of IIR digital filter by using RGA. RGA works on real numbers and is a powerful global optimization algorithm. To obtain minimum transition band, this designing of low pass IIR digital filter is proposed. It is observed that the calculated values are more optimal than those which are obtained by FDA tool available for design of filter in MATLAB. The improved mean-square-error (MSE) is observed in simulation results for example taken.


  1. W.S. Lu, W.-S. Lu, S. Pei, and C. Tseng, “A weighted least-squares method for the design of stable 1-D and 2-D IIR digital filters,” IEEE Trans. Signal Process., vol. 46, no. 1, pp. 1–10, Jan. 1998.
  2. C. Tseng and S. Lee, “Minimax design of stable IIR digital filter with prescribed magnitude and phase responses,” IEEE Trans. Circuits Syst. I, Reg. Papers, vol. 49, no. 4, pp. 547–551, April 2002.
  3. C. Tseng, “Design of stable IIR digital filter based on least p-power error criterion,” IEEE Trans. Circuits Syst. I, Reg. Papers, vol. 51, no.9, pp. 1879–1888, Sept. 2004.
  4. S.H. Ling, H.H.C. Iu, F.H.F. Leung, and K.Y. Chan, “Improved hybrid particle swarm optimized wavelet neural network for modeling the development of fluid dispensing for electronic packaging,” IEEE Trans. Ind. Electron., vol. 55, no. 9, pp. 3447–3460, Sept. 2008.
  5. K.S. Tang, K.F. Man, S. Kwong and Z.F. Liu, “Design and optimization of IIR filter structure using hierarchal genetic algorithms, ”IEEE Transactions on Industrial Electronics, vol. 45, no. 3, pp. 481–487, June 1998.
  6. D.E. Goldberg, “Genetic algorithms in search, optimization, and machine learning,” Addison-Wesley: New York, 1989.
  7. R. Chiong and O.K. Beng, “A Comparison between genetic algorithms and evolutionary programming based on cutting ctock problem,” Engineering Letters, vol. 14, issue 1, Feb. 2007, pp72-77.
  8. Terry Jones, Stephanie Forrest, “Genetic algorithms and heuristic search,” SFI working paper:1995-02-021
  9. Mehrdad Dianati, Inop Song and Mark Treiber, “An introduction to genetic algorithms and evolution strategies,” University of Waterloo, Ontario, N2L 3G1, Canada.
  10. Melanie Mitchell, “Genetic algorithms: an overview,” Santa Fe Institute, Comlexity, 1 (1) 31-39, 1995, Adapted from An Introduction to Genetic Algorithms, Chapter 1, MIT Press.
  11. T.A. El-Mihoub, A.A. Hopgood, L.S Nolle and A. Battersby, “Hybrid genetic algorithms: a Review,” Engineering Letters, vol. 13, issue 2, Aug. 2006, pp124-137.


Infinite Impulse Response (IIR) filter, Real Coded Genetic Algorithm (RGA/RCGA), Stability