Challenging the Evolutionary Strategy for Synthesis of Analogue Computational Circuits
ABSTRACT
There are very few reports in the past on applications of Evolutionary Strategy (ES) towards the synthesis of analogue circuits. Moreover, even fewer reports are on the synthesis of computational circuits. Last fact is mainly due to the difficulty in designing of the complex nonlinear functions that these circuits perform. In this paper, the evolving power of the ES is challenged to design four computational circuits: cube root, cubing, square root and squaring functions. The synthesis succeeded due to the usage of oscillating length genotype strategy and the substructure reuse. The approach is characterized by its simplicity and represents one of the first attempts of application of ES towards the synthesis of “QR” circuits. The obtained experimental results significantly exceed the results published before in terms of the circuit quality, economy in components and computing resources utilized, revealing the great potential of the technique proposed to design large scale analog circuits.
There are very few reports in the past on applications of Evolutionary Strategy (ES) towards the synthesis of analogue circuits. Moreover, even fewer reports are on the synthesis of computational circuits. Last fact is mainly due to the difficulty in designing of the complex nonlinear functions that these circuits perform. In this paper, the evolving power of the ES is challenged to design four computational circuits: cube root, cubing, square root and squaring functions. The synthesis succeeded due to the usage of oscillating length genotype strategy and the substructure reuse. The approach is characterized by its simplicity and represents one of the first attempts of application of ES towards the synthesis of “QR” circuits. The obtained experimental results significantly exceed the results published before in terms of the circuit quality, economy in components and computing resources utilized, revealing the great potential of the technique proposed to design large scale analog circuits.
Cite this paper
nullY. Sapargaliyev and T. Kalganova, "Challenging the Evolutionary Strategy for Synthesis of Analogue Computational Circuits," Journal of Software Engineering and Applications, Vol. 3 No. 11, 2010, pp. 1032-1039. doi: 10.4236/jsea.2010.311121.
nullY. Sapargaliyev and T. Kalganova, "Challenging the Evolutionary Strategy for Synthesis of Analogue Computational Circuits," Journal of Software Engineering and Applications, Vol. 3 No. 11, 2010, pp. 1032-1039. doi: 10.4236/jsea.2010.311121.
References
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[1] A. Thompson, “Artificial Evolution in the Physical World,” In: Gomi, Ed., Evolutionary Robotics, AAI Books, 1997, pp. 101-125. [2] R. Ingo, “Optimierung Technischer Systeme Nach Prinzipien der Biologischen Evolution,” PhD Dissertation, Technical University of Berlin, 1970. [3] W. Mydlowec and J. Koza, “Use of Time-Domain Simulations in Automatic Synthesis of Computational Circuits Using Genetic Programming,” Genetic and Evolutionary Computation Conference, Las Vegas, 2000, pp. 187-197. [4] T. McConaghy and G. G. E. Gielen, “Globally Reliable Variation-Aware Sizing of Analog Integrated Circuits via ResponseSurfaces and Structural Homotopy,” IEEE Transactions on Computer-Aided Design, Vol. 28, No. 11, November 2009, pp.1627-1640. [5] K. Kim, A. Wong and H. Lipson, “Automated Synthesis of Resilient and Tamper-Evident Analog Circuits without a Single Point of Failure,” Genetic Programming and Evolvable Machines (online), 2009. [6] A. Thompson and P. Layzell, “Evolution of Robustness in an Electronics Design,” International Conference on Intelligent Engineering Systems, Springer, 2000, pp. 218-228. [7] A. Stoica, D. Keymeulen, T. Arslan, V. Duong, R. Zebulum, I. Ferguson and X. Guo, “Circuit Self-Recovery Experiments in Extreme Environments,” Proceedings of the 2004 NASA/DoD Conference on Evolvable Hardware, Seattle, June 2004, pp. 142-145 [8] J. Walker, J. Hilder and A. Tyrrell, “Towards Evolving Industry-Feasible Intrinsic Variability Tolerant CMOS Designs,” 11th IEEE Congress on Evolutionary Computation, Trondheim, May 2009, pp. 1591-1598. [9] J. Koza, “Automated Synthesis of Computational Circuits Using Genetic Programming,” IEEE Conference on Evolutionary Computation, Piscataway, 1997, pp. 447-452. [10] R. Zebulum, M. Pacheco and M. Vellasco, “Comparison of Different Evolutionary Methodologies Applied to Electronic Filter Design,” IEEE Conference on Evolutionary Computation, Piscataway, 1998, pp. 434-439. [11] J. Lohn and S. Colombano, “Automated Analog Circuit Synthesis Using a Linear Representation,” The 2nd International Conference on Evolvable Systems: From Biology to Hardware, Springer-Verlag, 1998, pp. 125-133. [12] C. Goh and Y. Li, “GA Automated Design and Synthesis of Analog Circuits with Practical Constraints,” Proceedings of the 2001 Congress on Evolutionary Computation, Vol. 1, 2001, pp. 170-177. [13] S. Ando and H. Iba, “Analog Circuit Design with a Variable Length Chromosome,” Proceedings of the 2000 Congress on Evolutionary Computation, Vol. 2, 2000, pp. 994-1001. [14] J. Grimbleby, “Hybrid Genetic Algorithms for Analogue Network Synthesis,” Proceedings of the 1999 Congress on Evolutionary Computation, 1999, pp. 1781-1787. [15] J. Hu, X. Zhong and E. Goodman, “Open-ended Robust Design of Analog Filters Using Genetic Programming,” Genetic & Evolutionary Computation Conference, Vol. 2, 2005, pp. 1619-1626. [16] Y. Sapargaliyev and T. Kalganova, “On Comparison of Constrained and Unconstrained Evolutions in Analogue Electronics on the Example of LC Low-Pass Filters,” IEICE Transactions on Electronics, Vol. E89-C, No. 12, December 2006, pp. 1920-1927. [17] Z. Gan, Z. Yang, G. Li and M. Jiang, “Automatic Synthesis of Practical Passive Filters Using Clonal Selection Principle-Based Gene Expression Programming,” Proceedings of the 7th International Conference on Evolvable Systems: From Biology to Hardware (ICES'07), Vol. 4684, 2007, pp. 1611-3349. [18] S. Chang, H. Hou and Y. Su, “Automated Passive Filter Synthesis Using a Novel Tree Representation and Genetic Programming,” IEEE Transactions on Evolutionary Computation, Vol. 10, No. 1, February 2006, pp. 93-100. [19] D. Smith, “A Square Root Circuit to Linearize Feedback in Temperature Controllers,” Journal of Physics E: Scientific Instruments, Vol. 5, No. 6, 1972, pp. 528-529. [20] M. Streeter, M. Keane and J. Koza, “Iterative Refinement of Computational Circuits Using Genetic Programming,” Proceeding of the 2002 Genetic and Evolutionary Computation Conference, 2002, pp 877-884. [21] T. Dastidar, P. Chakrabarti and P. Ray, “A synthesis System for Analog Circuits Based on Evolutionary Search and Topological Reuse,” IEEE Transactions on Evolutionary Computation, Vol. 9, No. 2, 2005, pp. 211-224. [22] T. Sripramong and C. Toumazou, “The Invention of CMOS Amplifiers Using Genetic Programming and Current-Flow Analysis,” IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems, Vol. 21, No. 11, 2002, pp.1237-1252. [23] C. Mattiussi and D. Floreano, “Analog Genetic Encoding for the Evolution of Circuits and Networks,” IEEE Tran- sactions on Evolutionary Computation, Vol. 11, No. 5, 2007, pp. 596-607. [24] A. Das and R. Vemuri, “An Automated Passive Analog Circuit Synthesis Framework using Genetic Algorithms,” Proceedings of the IEEE Computer Society Annual Symposium on VLSI, 2007, pp. 145-152. [25] S. Cipriani and A. Takeshian, “Compact Cubic Function Generator,” U. S. Patent 6,160,427, Filed 4 September 1998, Issued December 12, 2000. [26] OrCAD, Inc. “OrCad PSpice User’s Guide,” OrCAD, USA, 2004. http://www.electronics-lab.com/downloads/ schematic/013/tutorial/PSPCREF.pdf [27] J. Koza, L. Jones, M. Keane, M. Streeter and S. Al-Sakran, “Toward Automated Design of Industrial-Strength Analog Circuits by Means of Genetic Programming,” Genetic Programming Theory and Practice II, Kluwer Academic Publishers, Boston, 2004.