When estimating how much better a classifier is than random allocation in Q-class ROC analysis, we need to sample from a particular region of the unit hypercube: specifically the region, in the unit hypercube, which lies between the Q− 1 simplex in Q(Q− 1) space and the origin. This report introduces a fast method for randomly sampling this volume, and is compared to rejection sampling of unifo...

Hull form optimisation involves challenges such as large design spaces, numerous variables, and high nonlinearity. Therefore, that only use global approximate models alone cannot yield desirable results. An information matrix-based method is proposed for dynamically embedded local (IM-DEAM) in this paper, which uses the Gaussian-function matrix to extract one or more subspaces additional sampli...

Memetic algorithms, one type of algorithms inspired by nature, have been successfully applied to solve numerous optimization problems in diverse fields. In this paper, we propose a new memetic computing model, using a hierarchical particle swarm optimizer (HPSO) and latin hypercube sampling (LHS) method. In the bottom layer of hierarchical PSO, several swarms evolve in parallel to avoid being t...

This paper investigates the variance reduction techniques Antithetic Variates (AV) and Latin Hypercube Sampling (LHS) when used for sequential sampling in stochastic programming presents a comparative computational study. It shows conditions under which with AV LHS satisfy finite stopping guarantees are asymptotically valid, discussing detail. computationally compares their use both non-sequent...

Spatiotemporally representative Elementary Sampling Units (ESUs) are required for capturing the temporal variations in surface spatial heterogeneity through field measurements. Since inaccessibility often coexists with heterogeneity, a cost-efficient sampling design is mandatory. We proposed a sampling strategy to generate spatiotemporally representative and cost-efficient ESUs based on the con...