Abstract
The two dimensional IIR digital filter design problem has received increased attention over the past few years. Recently, several metaheuristic algorithms have been employed in this domain and have produced promising results. Invasive Weed Optimization is one of the latest population-based metaheuristic algorithms that mimics the colonizing action of weeds. In this chapter, an improvement to the classical weed optimization algorithm has been proposed by introducing a constriction factor in the seed dispersal phase. Temporal Difference Q-Learning has been employed to adapt this parameter for different population members through the successive generations. Such hybridization falls under a special class of adaptive Memetic Algorithms. The proposed memetic realization, called Intelligent Invasive Weed Optimization (IIWO), has been applied to the two-dimensional recursive digital filter design problem and it has outperformed several competitive algorithms that have been applied in this research field in the past.
Keywords
- Dimensional Recursion
- Invasive Weed Optimization (IWO)
- Adaptive Memetic Algorithm (AMA)
- Filter Design Problem
- Population Members
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
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References
Bhowmik, P., Rakshit, P., Konar, A., Kim, E., Nagar, A.K.: DE-TDQL: an adaptive memetic algorithm. In: 2012 IEEE Congress on Evolutionary Computation (CEC) (2012)
Chen, X., Ong, Y.-S., Lim, M.-H., Tan, K.C.: A multi-facet survey on memetic computation. IEEE Trans. Evol. Comput. 15(5), 591–607 (2011)
Daniel, M., Willsky, A.: Efficient implementations of 2-D noncausal IIR filters. IEEE Trans. Circuits Syst. II, Analog Digit. Signal Process. 44(7), 549–563 (1997)
Das, S., Konar, A.: A swarm intelligence approach to the synthesis of two-dimensional IIR filters. Eng. Appl. Artif. Intell. 20, 1086–1096 (2007)
Dawkins, R.: The Selfish Gene. Oxford University Press, Oxford (1976)
Deb, K.: An efficient constraint handling method for genetic algorithms. Comput. Methods Appl. Mech. Eng. 186, 311–338 (1998)
Dumitrescu, B.: Optimization of two-dimensional IIR filters with non-separable and separable denominator. IEEE Trans. Signal Process. 53(5), 1768–1777 (2005)
Gonos, I.F., Virirakis, L.I., Mastorakis, N.E., Swamy, M.N.S.: Evolutionary design of 2-dimensional recursive filters via the computer language GENETICA. IEEE Trans. Circuits Syst. II, Express Briefs 53(4), 254–258 (2006)
Gonzales, R.C., Woods, R.E.: Digital Image Processing, 3rd edn. Addison-Wesley, Reading (1992)
Hsieh, C.-H., Kuo, C.-M., Jou, Y.-D., Han, Y.-L.: Design of two-dimensional FIR digital filters by a two-dimensional WLS technique. IEEE Trans. Circuits Syst. II, Analog Digit. Signal Process. 44(5), 348–412 (1997)
Kaczorek, T.: Two-Dimensional Linear Systems. Springer, Berlin (1985)
Kendall, G., Cowling, P., Soubeiga, E.: Choice function and random hyperheuristics. In: Proceedings of the 4th Asia-Pacific Conference on Simulated Evolution and Learning, pp. 667–671 (2002)
Laasko, T., Ovaska, S.: Design and implementation of efficient IIR notch filters with quantization error feedback. IEEE Trans. Instrum. Meas. 43(3), 449–456 (1994)
Lu, W.-S., Antoniou, A.: Two-Dimensional Digital Filters. Marcel Dekker, New York (1992)
Maria, G.A., Fahmy, M.M.: An LP design technique for two-dimensional digital recursive filters. IEEE Trans. Acoust. Speech Signal Process. ASSP-22(1), 15–21 (1974)
Mastorakis, N.E., Gonos, I.F., Swamy, M.N.S.: Design of 2-dimensional recursive filters using genetic algorithms. IEEE Trans. Circuits Syst. I, Fundam. Theory Appl. 50(5), 634–639 (2003)
Mehrabian, A.R., Lucas, C.: A novel numerical optimization algorithm inspired from weed colonization. Ecol. Inform. 1, 355–366 (2006)
Mladenov, V., Mastorakis, N.: Design of two-dimensional recursive filters by using neural networks. IEEE Trans. Neural Netw. 12(3), 585–590 (2001)
Moscato, P.: On evolution, search, optimization, genetic algorithms and martial arts: towards memetic algorithms. In: Caltech Concurrent Computation Program (report 826)
Ong, Y.-S., Lim, M.H., Zhu, N., Wong, K.-W.: Classification of adaptive memetic algorithms: a comparative study. IEEE Trans. Syst. Man Cybern. 36, 1 (2006)
Oppenheim, A.V., Schafer, R.W., Buck, J.R.: Discrete-Time Signal Processing. Prentice-Hall, Englewood Cliffs (1999)
Proakis, J.G., Manolakis, D.G.: Digital Signal Processing. Prentice-Hall, Englewood Cliffs (1996)
Rajan, P.K., Swamy, M.N.S.: Quadrantal symmetry associated with two-dimensional digital transfer functions. IEEE Trans. Circuits Syst. CAS-29(6), 340–343 (1983)
Sengupta, A., Chakraborti, T., Konar, A., Kim, E., Nagar, A.K.: An adaptive memetic algorithm using a synergy of differential evolution and learning automata. In: 2012 IEEE Congress on Evolutionary Computation (CEC) (2012)
Sengupta, A., Chakraborti, T., Konar, A., Nagar, A.K.: A multi-objective memetic optimization approach to the circular antenna array design problem. Prog. Electromagn. Res. B 42, 363–380 (2012)
Tzafestas, S.G. (ed.): Multidimensional Systems, Techniques and Applications. Marcel Dekker, New York (1986)
Watkins, C.: Learning from delayed rewards. PhD dissertation, King’s College, Cambridge, England (1989)
Watkins, C., Dayan, P.: Q-learning. Mach. Learn. 8, 279–292 (1992)
Zhu, W.-P., Ahmad, M.O., Swamy, M.N.S.: A closed-form solution to the least-square design problem of 2-D linear-phase FIR filters. IEEE Trans. Circuits Syst. II, Analog Digit. Signal Process. 44(12), 1032–1039 (1997)
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Sengupta, A., Chakraborti, T., Konar, A. (2013). A Metaheuristic Approach to Two Dimensional Recursive Digital Filter Design. In: Chatterjee, A., Nobahari, H., Siarry, P. (eds) Advances in Heuristic Signal Processing and Applications. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-37880-5_8
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