Classifier-guided sampling for discrete variable,discontinuous design space exploration: Convergence and computational performance |
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Authors: | Peter B Backlund David W Shahan |
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Affiliation: | 1. Sandia National Laboratories, Albuquerque, New Mexico, USA;2. HRL Laboratories, LLC, Malibu, California, USA |
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Abstract: | A classifier-guided sampling (CGS) method is introduced for solving engineering design optimization problems with discrete and/or continuous variables and continuous and/or discontinuous responses. The method merges concepts from metamodel-guided sampling and population-based optimization algorithms. The CGS method uses a Bayesian network classifier for predicting the performance of new designs based on a set of known observations or training points. Unlike most metamodelling techniques, however, the classifier assigns a categorical class label to a new design, rather than predicting the resulting response in continuous space, and thereby accommodates non-differentiable and discontinuous functions of discrete or categorical variables. The CGS method uses these classifiers to guide a population-based sampling process towards combinations of discrete and/or continuous variable values with a high probability of yielding preferred performance. Accordingly, the CGS method is appropriate for discrete/discontinuous design problems that are ill suited for conventional metamodelling techniques and too computationally expensive to be solved by population-based algorithms alone. The rates of convergence and computational properties of the CGS method are investigated when applied to a set of discrete variable optimization problems. Results show that the CGS method significantly improves the rate of convergence towards known global optima, on average, compared with genetic algorithms. |
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Keywords: | classifier-guided sampling sequential sampling metamodelling direct search stochastic optimization Bayesian classification |
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