In this paper, we present a Multi-Surrogates Assisted Memetic Algorithm (MSAMA) for solving optimization problems with computationally expensive fitness functions. The essential backbone of our framework is an evolutionary algorithm coupled with a local search solver that employs multi-surrogates in the spirit of Lamarckian learning. Inspired by the notion of 'blessing and curse of uncertainty'...

Local Derivative-Free Approximation of Computationally Expensive Posterior Densities Nikolay Bliznyuk a , David Ruppert b & Christine A. Shoemaker c a Department of Statistics, University of Florida, Gainesville, FL, 32611 b School of Operations Research and Information Engineering, Cornell University, Ithaca, NY, 14853 c School of Civil and Environmental Engineering, and School of Operations R...

The use of statistical models to approximate detailed analysis codes for evolutionary optimization has attracted some attention [1-3]. However, those early methodologies do suffer from some limitations, the most serious of which being the extra tuning parameter introduceds. Also the question of when to include more data points to the approximation model during the search remains unresolved. Tho...

Bayesian MCMC calibration and uncertainty analysis for computationally expensive models is implemented using the SOARS (Statistical and Optimization Analysis using Response Surfaces) methodology. SOARS uses a radial basis function interpolator as a surrogate, also known as an emulator or meta-model, for the logarithm of the posterior density. To prevent wasteful evaluations of the expensive mod...

Fast inference of numerical model parameters from data is an important prerequisite to generate predictive models for a wide range applications. Use sampling-based approaches such as Markov chain Monte Carlo may become intractable when each likelihood evaluation computationally expensive. New combining variational with normalizing flow are characterized by computational cost that grows only lin...

• Novel constrained grey-box optimization framework using Gaussian process models. New almost everywhere differentiable acquisition function for composite functions. Efficient moment-based approximation of chance constraints. Tailored algorithm enrichment sub-problem that exploits model structure. Performance comparison with Bayesian on diverse set test problems. Many engineering problems invol...

Abstract Surrogate-assisted evolutionary algorithms have been paid more and attention to solve computationally expensive problems. However, model management still plays a significant importance in searching for the optimal solution. In this paper, new method is proposed measure approximation uncertainty, which differences between solution its neighbour samples decision space, ruggedness of obje...