Advertisement

Hybrid Genetic Algorithm for VLSI Macro Cell Layout

  • S. Sathiamoorthy
  • G. Andaljayalakshmi

Abstract

Genetic algorithms have proven to be a well-suited technique for solving selected combinatorial optimization problems. The blindness of the algorithm during the search in the space of encoding must be abandoned, because this space is discrete and the search has to reach feasible points after the application of the genetic operators. This can be achieved by the use of a problem specific genotype encoding, and hybrid, knowledge based techniques, which support the algorithm during the creation of the initial individuals and the following optimization process. In this paper a novel hybrid genetic algorithm, which is used to solve macro-cell placement problem is presented. Two new heuristics are introduced. Due to a tree-structured genotype representation and hybrid, problem- specific operators, the proposed approach is able to show satisfactory performance.

Keywords

Genetic Algorithm Binary Tree Mutation Operator Combinatorial Optimization Problem Genetic Operator 
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.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Lienig J. (1997) A Parallel Genetic Algorithm for Performance Driven VLSI Routing. IEEE Transactions on Evolutionary Computation Vol. I. No.1:29–39CrossRefGoogle Scholar
  2. 2.
    Mazumder P., Rudnick E. (1999) Genetic Algorithm for VLSI Design, Layout and Automation. Addison-Wesley Longman Singapore Pte. Ltd., Singapore.Google Scholar
  3. 3.
    MCNC Benchmarks: www.cse.ucsc.edu/research/surf/GSRC/MCNC/Google Scholar
  4. 4.
    Schnecke V., Vomberger O. (1996) A Genetic Algorithm for VLSI Physical Design Automation:In Proceedings of Second Int. Conf. on Adaptive Computing in Engineering Design and Control, ACEDC’96 26-28 Mar 1996, University of Plymouth, U.K., pp 53–58Google Scholar
  5. 5.
    Schnecke V., Vornberger O (1996) An Adaptive Parallel Genetic Algorithm for VLSILayout Optimization:In Proceedings of 4th Int. Conf. on Parallel Problem Solving from Nature (PPSN IV) 22-27 Sep 1996, Springer LNCS 1141, pp 859–868Google Scholar
  6. 6.
    Schnecke V., Vornberger O (1997) Hybrid Genetic Algorithms for Constrained Placement Problems. IEEE Transactions on Evolutionary Computation. Vol. I. No.4.:266–277CrossRefGoogle Scholar
  7. 7.
    Xu, J., Guo P., et al. (1997) Cluster Refinement for Block Placement: In Proceedings of ACM-DAC California,paper 47.4Google Scholar

Copyright information

© Springer-Verlag London 2002

Authors and Affiliations

  • S. Sathiamoorthy
    • 1
  • G. Andaljayalakshmi
    • 1
  1. 1.Department of Computer Science and EngineeringThiagarajar College of EngineeringMaduraiIndia

Personalised recommendations