GEORG: VLSI circuit partitioner with a new genetic algorithm framework

This paper suggests a new framework of multidimensional genetic algorithm and applies it to the real-world problem of very large scale integration (VLSI) partitioning. The framework consists of a new multidimensional genetic operator, called geographic crossover, and a new genetic encoding scheme. Geographic crossover enables more powerful creation of new solutions by allowing a diverse mixture of parent solutions. Its theoretical validity is proved based on a new view of crossover. The new genetic encoding scheme helps space search by effectively utilizing geographical linkages of genes. The new framework can be incorporated into most existing genetic algorithm (GA) implementations just by replacing the crossover module and leaving the other modules intact. For a test suite of 11 ACM/SIGDA VLSI circuit␣partitioning benchmark circuits, the GA under this framework significantly outperformed recently published state-of-the-art methods as well as a previous GA on linear string.

[1]  Nathan Linial,et al.  The geometry of graphs and some of its algorithmic applications , 1994, Proceedings 35th Annual Symposium on Foundations of Computer Science.

[2]  John Daniel. Bagley,et al.  The behavior of adaptive systems which employ genetic and correlation algorithms : technical report , 1967 .

[3]  John Holland,et al.  Adaptation in Natural and Artificial Sys-tems: An Introductory Analysis with Applications to Biology , 1975 .

[4]  Nicholas J. Radcliffe,et al.  Equivalence Class Analysis of Genetic Algorithms , 1991, Complex Syst..

[5]  Melanie Mitchell,et al.  What makes a problem hard for a genetic algorithm? Some anomalous results and their explanation , 1993, Machine Learning.

[6]  Byung Ro Moon,et al.  A Fast and Stable Hybrid Genetic Algorithm for the Ratio-Cut Partitioning Problem on Hypergraphs , 1994, 31st Design Automation Conference.

[7]  Gilbert Syswerda,et al.  Uniform Crossover in Genetic Algorithms , 1989, ICGA.

[8]  R. M. Mattheyses,et al.  A Linear-Time Heuristic for Improving Network Partitions , 1982, 19th Design Automation Conference.

[9]  John H. Holland,et al.  Adaptation in Natural and Artificial Systems: An Introductory Analysis with Applications to Biology, Control, and Artificial Intelligence , 1992 .

[10]  Anant Agarwal,et al.  Limits on Interconnection Network Performance , 1991, IEEE Trans. Parallel Distributed Syst..

[11]  Byung Ro Moon,et al.  GRCA: a hybrid genetic algorithm for circuit ratio-cut partitioning , 1998, IEEE Trans. Comput. Aided Des. Integr. Circuits Syst..

[12]  William J. Dally,et al.  Performance Analysis of k-Ary n-Cube Interconnection Networks , 1987, IEEE Trans. Computers.

[13]  Darrell Whitley,et al.  Genitor: a different genetic algorithm , 1988 .

[14]  Byung Ro Moon,et al.  Analyzing Hyperplane Synthesis in Genetic Algorithms Using Clustered Schemata , 1994, PPSN.

[15]  Charles M. Fiduccia,et al.  A linear-time heuristic for improving network partitions , 1988, 25 years of DAC.

[16]  D. E. Goldberg,et al.  Genetic Algorithms in Search , 1989 .

[17]  Pinaki Mazumder,et al.  Macro-cell and module placement by genetic adaptive search with bitmap-represented chromosome , 1991, Integr..

[18]  Andrew B. Kahng,et al.  New spectral methods for ratio cut partitioning and clustering , 1991, IEEE Trans. Comput. Aided Des. Integr. Circuits Syst..

[19]  James P. Cohoon,et al.  Genetic Placement , 1987, IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems.

[20]  Andrew B. Kahng,et al.  Toward More Powerful Recombinations , 1995, ICGA.

[21]  Byung Ro Moon,et al.  On Multi-Dimensional Encoding/Crossover , 1995, ICGA.

[22]  Byung Ro Moon,et al.  Hyperplane Synthesis for Genetic Algorithms , 1993, ICGA.

[23]  Chung-Kuan Cheng,et al.  Ratio cut partitioning for hierarchical designs , 1991, IEEE Trans. Comput. Aided Des. Integr. Circuits Syst..

[24]  Lawrence. Davis,et al.  Handbook Of Genetic Algorithms , 1990 .

[25]  Carl Sechen,et al.  Efficient and effective placement for very large circuits , 1995, IEEE Trans. Comput. Aided Des. Integr. Circuits Syst..

[26]  Byung Ro Moon,et al.  Genetic VLSI circuit partitioning with two-dimensional geographic crossover and zigzag mapping , 1997, SAC '97.

[27]  John H. Holland,et al.  Adaptation in Natural and Artificial Systems: An Introductory Analysis with Applications to Biology, Control, and Artificial Intelligence , 1992 .

[28]  Jennifer Ryan,et al.  A Two-Dimensional Genetic Algorithm for the Ising Problem , 1991, Complex Syst..

[29]  David E. Goldberg,et al.  Genetic Algorithms in Search Optimization and Machine Learning , 1988 .

[30]  Byung Ro Moon,et al.  Genetic Algorithm and Graph Partitioning , 1996, IEEE Trans. Computers.

[31]  Nostrand Reinhold,et al.  the utility of using the genetic algorithm approach on the problem of Davis, L. (1991), Handbook of Genetic Algorithms. Van Nostrand Reinhold, New York. , 1991 .