Evolutionary Programming Based Stratified Design Space Sampling

Recently there have been advances in strati ed sampling techniques that attempt to enforce equal distributions not only across the design variables, but also onto the design space itself. This requires a numerically intensive optimization routine. Until now, no optimization strategy was able to distribute sample points evenly in the design space, but Evolutionary Algorithms (EA) act as an enabling technology to spread in such a way that the minimum distance between points is near ideal for the design space. The proposed technique is applied to a standard probabilistic analysis problem, a one-degree of freedom oscillator simulating a blade near harmonic resonance. The Evolutionary Programming results compared to Latin Hypercube Sampling indicate a better estimate with a smaller con dence interval.