alexa PROCEEDINGS OF THE NINTH INTERNATIONAL CONFERENCE ON THE APPLICATION OF ARTIFICIAL INTELLIGENCE TO CIVIL, STRUCTURAL AND ENVIRONMENTAL ENGINEERING
Engineering

Engineering

Advances in Automobile Engineering

Author(s): VV Toropov, SJ Bates

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The choice of location of the evaluation points or plan points is important in getting a good approximation of a system's response, especially when evaluations are expensive. Space-filling designs of experiments (DOE) can be used to specify the points so that as much of the design space is sampled as possible with the minimum number of response evaluations. One popular technique is the optimal Latin hypercube (OLH) design of experiments. However, its generation is non-trivial, time consuming and is - but for the simplest problems - infeasible to carry out by enumeration. Therefore, solving this problem requires an optimization technique to search the design space. As the problem is discrete, it is ideally suited to the use of discrete optimization techniques such as genetic algorithms (GAs). A method has been developed for formulating the OLH DOE using the Audze-Eglais objective function [1]. It has been shown that the formulation of OLHs is ideally suited to using permutation form of GA [2,3] since the problem uses discrete design variables and the LH requires that values representing DOE levels in a chromosome are not repeated.

This article was published in Computational & Technology Resources and referenced in Advances in Automobile Engineering

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