VLSI design optimization requires evaluation of different solutions, to determine the superiority of one over the other. Typically, a solution is superior if it has a better associated timing and cost. In the presence of fabrication variability, the timing and cost of a solution become random variables with spatial and functional correlations. Therefore the evaluation of solutions shall be perf...

In this paper, we present an adaptive evolutionary Monte Carlo algorithm (AEMC), which combines a treebased predictive model with an evolutionary Monte Carlo sampling procedure for the purpose of global optimization. Our development is motivated by sensor placement applications in engineering, which requires optimizing certain complicated “black-box” objective function. The proposed method is a...