Application of the univariate marginal distribution algorithm to analog circuit design

The approach to computer aided analog circuit design on the base of univariate algorithms was derived by analysing the mathematical principles behind recombination. A Bayesian prior used for the estimations of the probability distribution is equivalent to having mutation for the genetic algorithms. In this paper the relation between a success rate and a mutation one is considered for analog circuit design. Practical illustration of the use of this approach is demonstrated for filter design. Experiments indicate that mutation and elitism increase the performance of the algorithms and decrease the dependence of the correct choice of the population size.

[1]  T. Mahnig,et al.  Evolutionary algorithms: from recombination to search distributions , 2001 .

[2]  Yun Li,et al.  GA automated design and synthesis of analog circuits with practical constraints , 2001, Proceedings of the 2001 Congress on Evolutionary Computation (IEEE Cat. No.01TH8546).

[3]  Jason D. Lohn,et al.  A circuit representation technique for automated circuit design , 1999, IEEE Trans. Evol. Comput..

[4]  L. Kallel,et al.  Theoretical Aspects of Evolutionary Computing , 2001, Natural Computing Series.

[5]  H. Mühlenbein,et al.  From Recombination of Genes to the Estimation of Distributions I. Binary Parameters , 1996, PPSN.

[6]  Raoul Tawel,et al.  Evolutionary experiments with a fine-grained reconfigurable architecture for analog and digital CMOS circuits , 1999, Proceedings of the First NASA/DoD Workshop on Evolvable Hardware.

[7]  Rob A. Rutenbar,et al.  Synthesis tools for mixed-signal ICs: progress on frontend and backend strategies , 1996, DAC '96.

[8]  John R. Koza,et al.  Automated synthesis of analog electrical circuits by means of genetic programming , 1997, IEEE Trans. Evol. Comput..

[9]  J. B. Grimbleby,et al.  Hybrid genetic algorithms for analogue network synthesis , 1999, Proceedings of the 1999 Congress on Evolutionary Computation-CEC99 (Cat. No. 99TH8406).

[10]  Heinz Mühlenbein,et al.  Optimal Mutation Rate Using Bayesian Priors for Estimation of Distribution Algorithms , 2001, SAGA.

[11]  Kenji Toda,et al.  Real-world applications of analog and digital evolvable hardware , 1999, IEEE Trans. Evol. Comput..

[12]  Hitoshi Iba,et al.  Analog circuit design with a variable length chromosome , 2000, Proceedings of the 2000 Congress on Evolutionary Computation. CEC00 (Cat. No.00TH8512).

[13]  Domine Leenaerts,et al.  DARWIN: CMOS opamp Synthesis by Means of a Genetic Algorithm , 1995, 32nd Design Automation Conference.

[14]  Paul J. Layzell,et al.  Explorations in design space: unconventional electronics design through artificial evolution , 1999, IEEE Trans. Evol. Comput..

[15]  Adrian Stoica,et al.  Mixtrinsic Evolution , 2000, ICES.

[16]  John R. Koza.  Genetic Programming III - Darwinian Invention and Problem Solving , 1999, Evolutionary Computation.

[17]  Michiel Steyaert,et al.  Optimal RF design using smart evolutionary algorithms , 2000, Proceedings 37th Design Automation Conference.

[18]  Leon O. Chua,et al.  Computer-Aided Analysis Of Electronic Circuits , 1975 .

[19]  Marley M. B. R. Vellasco,et al.  Comparison of different evolutionary methodologies applied to electronic filter design , 1998, 1998 IEEE International Conference on Evolutionary Computation Proceedings. IEEE World Congress on Computational Intelligence (Cat. No.98TH8360).

[20]  Heinz Mühlenbein,et al.  The Equation for Response to Selection and Its Use for Prediction , 1997, Evolutionary Computation.

[21]  Lyudmila Zinchenko,et al.  Evolutionary design of electronic devices , 2000, ICECS 2000. 7th IEEE International Conference on Electronics, Circuits and Systems (Cat. No.00EX445).

[22]  Peter J. Bentley,et al.  Evolutionary Design By Computers , 1999 .