APU Performance Evaluation for Accelerating Computationally Expensive Workloads

Accelerating Bayesian Inference in Computationally Expensive Computer Models Using Local and Global Approximations

Computational models of complex phenomena are an important resource for scientists and engineers. However, many state-of-the-art simulations of physical systems are computationally expensive to evaluate and are black box—meaning that they can be run, but their internal workings cannot be inspected or changed. Directly applying uncertainty quantification algorithms, such as those for forward unc...

Multiple Objective Evolutionary Algorithms for Independent, Computationally Expensive Objective Evaluations

Landscape Features for Computationally Expensive Evaluation Functions: Revisiting the Problem of Noise

When combined with machine learning, the black-box analysis of fitness landscapes promises to provide us with easy-to-compute features that can be used to select and configure an algorithm that is well-suited to the task at hand. As applications that involve computationally expensive, stochastic simulations become increasingly relevant in practice, however, there is a need for landscape feature...

Memetic algorithm using multi-surrogates for computationally expensive optimization problems

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'...

Evolutionary Optimization for Computationally expensive problems using Gaussian Processes

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...