Here, we present an improved initialization method that overcomes these limitations by employing concepts from the design and analysis of computer experiments with branching and nested factors. incremental Latin-hypercube sampling method, adaptive discretization of. Although better initialization methods exist in the literature, the mixed-variable (numerical and categorical) nature of typical parameter spaces and the presence of conditional parameters make most of the methods not applicable in practice. Advances in multiscale methods are presented from two perspectives which address. The cumulative distribution is sampled using these values to determine the input variables. The ordering of the L realizations is then randomized. This ensures that each variable is uniformly sampled. By default, irace initializes its search process via uniform sampling of algorithm configurations. The Latin hypercube approach would be to generate NL uniform random numbers with a range given by Equation 2. Range of each design parameter is specified by defining its lower and upper bounds. Relationships between these parameters are also determined and are called design constraints. The CAD model is defined by design parameters. The irace method is among the most widely used in the literature. sampling in the constrained and high dimensional design spaces, which is named as Extended Latin Hypercube sampling (ELHS). The marginal proba-bility distribution for each parameter is stratied into equal probability bins, from which a Latin hypercube is constructed. These bounds will be used to quantify uncertainty in future simulations as they include error derived from data measurement, model structure and parameterization. sample the model output from some prior probability distribution on parameter space. Thus, it is advisable to automate this task by using appropriate automatic configuration methods. Furthermore we give conditions on the function f and the distribution of (U1,, Ud) under which a reduction of variance can be achieved. Uncertainty bounds are constructed for model output. The configuration of algorithms is a laborious and difficult process.

Latin hypercube sampling given bounds