Performance-driven MCM partitioning through an adaptive genetic algorithm

We present a novel genetic algorithm-based partitioning scheme for multichip modules (MCM's) which integrates four performance constraints simultaneously: pin count, area, heat dissipation, and timing. We also present a similar partitioning algorithm based on evolutionary programming. Experimental studies demonstrate the superiority of these methods over deterministic Fiduccia-Mattheyes (FM) algorithm and simulated annealing (SA) technique. Our approach performs better than another genetic algorithm-based method recently reported. The adaptive change of crossover and mutation probabilities results in better convergence of the partitioning algorithm.