Multiple-Way Network Partitioning
IEEE Transactions on Computers
An evolution-based approach to partitioning ASIC systems
DAC '89 Proceedings of the 26th ACM/IEEE Design Automation Conference
Macro-cell and module placement by genetic adaptive search with bitmap-represented chromosome
Integration, the VLSI Journal
Adaptation in natural and artificial systems
Adaptation in natural and artificial systems
A genetic algorithm for macro cell placement
EURO-DAC '92 Proceedings of the conference on European design automation
Efficient partitioning of components
DAC '68 Proceedings of the 5th annual Design Automation Workshop
A linear-time heuristic for improving network partitions
DAC '82 Proceedings of the 19th Design Automation Conference
An Improved Min-Cut Algonthm for Partitioning VLSI Networks
IEEE Transactions on Computers
Netlist bipartitioning using particle swarm optimisation technique
International Journal of Artificial Intelligence and Soft Computing
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This research investigates a new software tool for Genetic Partitioning. The Genetic Algorithm is used to perform the partitioning with a significant improvement in result quality. Furthermore, it can optimize a cost function with multiple objectives and constraints. Separate algorithms have been developed, fine-tuned for bipartitioning and multiway partitioning. The bipartitioning problem is represented as a binary chromosome. Efficient bit-mask operations perform crossover, mutation, and net cut evaluation 32 bits at a time, without unpacking. The multiway partitioning algorithm has a global view of the problem, and generates/optimizes all the necessary partitions simultaneously. The algorithms were tested on the MCNC benchmark circuits, and the cut size obtained was lower than that for the conventional Fiduccia-Mattheyses algorithm.