Abstract
This paper describes an automated process for designing analog electrical circuits based on the principles of natural selection, sexual recombination, and developmental biology. The design process starts with the random creation of a large population of program trees composed of circuit-constructing functions. Each program tree specifies the steps by which a fully developed circuit is to be progressively developed from a common embryonic circuit appropriate for the type of circuit that the user wishes to design. The fitness measure is a user-written computer program that may incorporate any calculable characteristic or combination of characteristics of the circuit. The population of program trees is genetically bred over a series of many generations using genetic programming. Genetic programming is driven by a fitness measure and employs genetic operations such as Darwinian reproduction, sexual recombination (crossover), and occasional mutation to create offspring. This automated evolutionary process produces both the topology of the circuit and the numerical values for each component. This paper describes how genetic programming can evolve the circuit for a difficult-to-design low-pass filter.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Aaserud, O. and Nielsen, I. R.: 1995. Trends in current analog design: A panel debate. Analog Integrated Circuits and Signal Processing. 7(1) 5–9.
Andre, D. and Koza, J. R.: 1996, Parallel genetic programming: A scalable implementation using the transputer architecture, in P. J. Angeline and K. E. Kinnear Jr. (eds), Advances in Genetic Programming 2, MIT Press, Cambridge, MA.
Degrauwe, M.: 1987, IDAC: An interactive design tool for analog integrated circuits. II Journal of Solid State Circuits, 22,1106–1116.
Gruau, F.: 1992, Cellular Encoding of Genetic Neural Networks, Technical report 92-21, Laboratoire de l’Informatique du Parallélisme. Ecole Normale Supérieure de Lyon.
Gruau, F.: 1994, Genetic micro programming of neural networks, in K. E. Kinnear Jr. (ed.), Advances in Genetic Programming. MIT Press, Cambridge, MA, pp. 495–518.
Harjani, R., Rutenbar, R. A. and Carley, L. R.: 1989, OASYS: A framework for analog circuit synthesis. II Transactions on Computer Aided Design, 8, 1247–1266.
Higuchi, T., Niwa, T., Tanaka, H., Iba, H., de Garis, H. and Furuya, T.: 1993, Evolvable hardware-Genetic-based generation of electric circuitry at gate and hardware description language (HDL) levels, Electrotechnical Laboratory technical report 93-4, Tsukuba, Ibaraki, Japan.
Holland, J. H.: 1975, Adaptation in Natural and Artificial Systems: An Introductory Analysis with Applications to Biology, Control, and Artificial Intelligence, University of Michigan Press, Ann Arbor, MI. Second edn MIT Press, Cambridge, MA, 1992.
Koh, H. Y., Sequin, C. H. and Gray, P. R.: 1990, OPASYN: A compiler for MOS operational amplifiers, II Transactions on Computer Aided Design. 9, 113–125.
Koza, J. R.: 1992, Genetic Programming: On the Programming of Computers by Means of Natural Selection, MIT Press, Cambridge, MA.
Koza, J. R.: 1994, Genetic Programming II: Automatic Discovery of Reusable Programs. MIT Press, Cambridge, MA
Kruiskamp, W. and Leenaerts, D.: 1995, DARWIN: CMOS opamp synthesis by means of a genetic algorithm, Proceedings of the 32nd Design Automation Conference, Association for Computing Machinery, New York, NY, pp. 433–438.
Maulik, P. C. Carley, L. R., and Rutenbar, R. A.: 1992, A mixed-integer nonlinear programming approach to analog circuit synthesis, Proceedings of the 29th Design Automation Conference, II Press, Los Alamitos, CA, pp. 698–703.
Ning, Z., Kole, M., Mouthaan, T., and Wallings, H.: 1992, Analog circuit design automation for performance, Proceedings of the 14th CICC, II Press, New York, pp. 8.2.1–8.2.4.
Quartes, T., Newton, A. R., Pederson, D. O. and Sangiovanni-Vincentelli, A.: 1994, SPICE 3 Version 3F5 User’s Manual, Department of Electrical Engineering and Computer Science, University of California, Berkeley, California.
Rutenbar, R. A:. 1993, Analog design automation: Where are we? Where are we going? Proceedings of the 15th 11 CICC, II Press, New York, pp. 13.1.1–13.1.8.
Samuel, A. L.: 1959, Some studies in machine learning using the game of checkers, IBM Journal of Research and Development, 3(3), 210–229.
Zverev, A. I.: 1967, Handbook of Filter Synthesis, Wiley.
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 1996 Kluwer Academic Publishers
About this chapter
Cite this chapter
Koza, J.R., Bennett, F.H., Andre, D., Keane, M.A. (1996). Automated Design of Both the Topology and Sizing of Analog Electrical Circuits Using Genetic Programming. In: Gero, J.S., Sudweeks, F. (eds) Artificial Intelligence in Design ’96. Springer, Dordrecht. https://doi.org/10.1007/978-94-009-0279-4_9
Download citation
DOI: https://doi.org/10.1007/978-94-009-0279-4_9
Publisher Name: Springer, Dordrecht
Print ISBN: 978-94-010-6610-5
Online ISBN: 978-94-009-0279-4
eBook Packages: Springer Book Archive