Skip to main content

An Immune Genetic Algorithm with Orthogonal Initialization for Analog Circuit Design

  • Conference paper
  • 2605 Accesses

Part of the Lecture Notes in Computer Science book series (LNAI,volume 7390)

Abstract

Evolutionary design of circuits (EDC) is an important branch of evolvable hardware (EHW). It is proved to be able to provide more optimized and diversified designs of circuit than human designs. An Orthogonal Immune Genetic Algorithm (OIGA) is proposed in this paper to improve the speed and efficiency of analog circuit design. Several factors, including the orthogonal initialization, the cloning selection operator, the hyper mutation operator, and the immune memory operator, are incorporated in the operation of OIGA. This algorithm makes use of the orthogonal design to select initialization population in order to preserve the diversity in the feasible solution space. The proposed algorithm is applied in the design of filter circuit. The simulation results show that OIGA can find the optimal solutions. The performance of the OIGA is compared with the Adaptive Immune Genetic Algorithm (AIGA) in optimizing a filter circuit. The results show that the OIGA is effective in improving the converging speed and the efficiency of the analog circuit design.

Keywords

  • Evolutionary design of circuits
  • Analog circuit
  • Orthogonal immune genetic algorithm
  • Cloning selection

This is a preview of subscription content, access via your institution.

Buying options

Chapter
USD   29.95
Price excludes VAT (Canada)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD   39.99
Price excludes VAT (Canada)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD   54.99
Price excludes VAT (Canada)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Learn about institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Garis, H.D., Hardware, E.: The Genetic Programming of Darwin Machines. In: The Proceeding of International Conference on Artificial Neural Nets and Genetic Algorithms, Innsbruck, Austria, pp. 441–449 (1993)

    Google Scholar 

  2. Biondi, T., Ciccazzo, C., Cutello, V., Antona, S.D., Nicosia, G., Spinella, S.: Multi-Objective Evolutionary Algorithms and Pattern Search Methods for Circuit Design Prob-lems. J. Universal Comput. Sci. 12(4), 432–449 (2006)

    Google Scholar 

  3. Zhao, S., Du, Q., Liu, Z., Pan, X.: UDT-Based Multi-objective Evolutionary Design of Passive Power Filters of a Hybrid Power Filter System. In: Kang, L., Liu, Y., Zeng, S. (eds.) ICES 2007. LNCS, vol. 4684, pp. 309–318. Springer, Heidelberg (2007)

    CrossRef  Google Scholar 

  4. Takemura, K., Koide, T., Mattausch, H., Tsuji, T.: Analog-circuit-component Optimization with Genetic Algorithm. In: Proceedings of the 47th IEEE International Midwest Symposium on Circuits and Systems, pp. 489–492 (2004)

    Google Scholar 

  5. Hedayat, A.S., Sloane, N.J.A., Stufken, J.: Orthogonal Arrays: Theory and Applications. Springer, New York (1999)

    MATH  Google Scholar 

  6. Suman, B., Hoda, N., Jha, S.: Orthogonal Simulated Annealing for Multiobjective Optimization. Computers and Chemical Engineering 34, 1618–1631 (2010)

    CrossRef  Google Scholar 

  7. Tanaka: A comparative Study of GA and Orthogonal Experimental Design. In: Proc. IEEE Int. Conf. Evol. Comput., pp. 143–146 (1997)

    Google Scholar 

  8. Zeng, S.Y., Kang, L.S., Ding, L.X.: An Orthogonal Multi-objective Evolutionary Algorithm for Multi-objective Optimization Problems with Constraints. Evolutionary Computation 12(1), 77–98 (2004)

    CrossRef  Google Scholar 

  9. Xu, H.Q., Ding, Y.S., Hu, Z.H.: Adaptive Immune Genetic Algorithm for Logic Circuit Design. In: Proceedings of GEC, pp. 639–644 (2009)

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Editor information

Editors and Affiliations

Rights and permissions

Reprints and Permissions

Copyright information

© 2012 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Xu, HQ., Ding, YS., Liu, H., Li, XL. (2012). An Immune Genetic Algorithm with Orthogonal Initialization for Analog Circuit Design. In: Huang, DS., Ma, J., Jo, KH., Gromiha, M.M. (eds) Intelligent Computing Theories and Applications. ICIC 2012. Lecture Notes in Computer Science(), vol 7390. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-31576-3_8

Download citation

  • DOI: https://doi.org/10.1007/978-3-642-31576-3_8

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-31575-6

  • Online ISBN: 978-3-642-31576-3

  • eBook Packages: Computer ScienceComputer Science (R0)