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19 May 2011 Initial data sampling in design optimization
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Abstract
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.
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Hugh L. Southall and Terry H. O'Donnell "Initial data sampling in design optimization", Proc. SPIE 8059, Evolutionary and Bio-Inspired Computation: Theory and Applications V, 805909 (19 May 2011); https://doi.org/10.1117/12.883490
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