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19 May 2011Initial data sampling in design optimization
Evolutionary computation (EC) techniques in design optimization such as genetic algorithms (GA) or efficient global
optimization (EGO) require an initial set of data samples (design points) to start the algorithm. They are obtained by
evaluating the cost function at selected sites in the input space. A two-dimensional input space can be sampled using a
Latin square, a statistical sampling technique which samples a square grid such that there is a single sample in any given
row and column. The Latin hypercube is a generalization to any number of dimensions. However, a standard random
Latin hypercube can result in initial data sets which may be highly correlated and may not have good space-filling
properties. There are techniques which address these issues. We describe and use one technique in this paper.