In this paper we present an enhanced evolutionary algorithm (EA) to solve computationally expensive design optimization problems. In this algorithm we integrate a genetic algorithm (GA) with a local search method to expedite convergence of the GA. We first use a GA to generate a population of data by evaluating real functions, then we construct computationally cheap surrogate models based on th...

Designing and optimizing complex systems generally requires the use of numerical models. However, it is often too expensive to evaluate these models at each step of an optimization problem. Instead surrogate models can be used to explore the design space, as they are much cheaper to evaluate. Constructing a surrogate becomes challenging when different numerical models are used to compute the sa...

A number of meta-analytical methods have been proposed that aim to evaluate surrogate endpoints. Bivariate meta-analytical methods can be used to predict the treatment effect for the final outcome from the treatment effect estimate measured on the surrogate endpoint while taking into account the uncertainty around the effect estimate for the surrogate endpoint. In this paper, extensions to mult...

Most publications on surrogate models have focused either on the prediction quality or on the optimization performance. It is still unclear whether the prediction quality is indeed related to the suitability for optimization. Moreover, most of these studies only employ lowdimensional test cases. There are no results for popular surrogate models, such as kriging, for high-dimensional (n > 10) no...

A possible approach to Algorithm Selection and Configuration for continuous black box optimization problems relies on problem features, computed from a set of evaluated sample points. However, the computation of these features requires a rather large number of such samples, unlikely to be practical for expensive real-world problems. On the other hand, surrogate models have been proposed to tack...

This article describes model-based methods for global optimization. After introducing the global optimization framework, modeling approaches for stochastic algorithms are presented. We differentiate between models that use a distribution and models that use an explicit surrogate model. Fundamental aspects of and recent advances in surrogate-model based optimization are discussed. Strategies for...