GRCA: A hybrid genetic algorithm for circuit ratio-cut partitioning

Thang Nguyen Bui, Byung Ro Moon

Research output: Contribution to journalArticle

20 Citations (Scopus)

Abstract

A genetic algorithm for partitioning a hypergraph into two disjoint graphs of minimum ratio cut is presented. As the Fiduccia-Mattheyses graph partitioning heuristic turns out to be not effective when used in the context of a hybrid genetic algorithm, we propose a modification of the Fiduccia-Mattheyses heuristic for more effective and faster space search by introducing a number of novel features. We also provide a preprocessing heuristic for genetic encoding designed solely for hypergraphs which helps genetic algorithms exploit clustering information of input graphs. Supporting combinatorial arguments for the new preprocessing heuristic are also provided. Experimental results on industrial benchmarks circuits showed visible improvement over recently published algorithms with a lower growth rate of running time.

Original languageEnglish (US)
Pages (from-to)193-204
Number of pages12
JournalIEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems
Volume17
Issue number3
DOIs
StatePublished - Dec 1 1998

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Genetic algorithms
Networks (circuits)

All Science Journal Classification (ASJC) codes

  • Software
  • Computer Graphics and Computer-Aided Design
  • Electrical and Electronic Engineering

Cite this

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GRCA : A hybrid genetic algorithm for circuit ratio-cut partitioning. / Bui, Thang Nguyen; Moon, Byung Ro.

In: IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems, Vol. 17, No. 3, 01.12.1998, p. 193-204.

Research output: Contribution to journalArticle

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