A parallel optimal statistical design method based on genetic algorithm

Genetic Algorithms (GA), together with a boundary sampling strategy have been identified as a novel approach for optimal statistical design to achieve better performance and higher yield at a minimum cost. Due to the reduced number of circuit simulations, the proposed combination can provide a satisfactory model representation at improved computation speed for the selection of the response surface model function. In this paper, a number of possible approaches for parallelizing the GA operations is identified, and studied. The parallel GA was implemented on a parallel machine constructed from a cluster of networked workstations.