Multiple infill criterion-assisted hybrid evolutionary optimization for medium-dimensional computationally expensive problems

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...

A multi-fidelity surrogate-model-assisted evolutionary algorithm for computationally expensive optimization problems

Integrating data-driven surrogate models and simulation models of di erent accuracies (or delities) in a single algorithm to address computationally expensive global optimization problems has recently attracted considerable attention. However, handling discrepancies between simulation models with multiple delities in global optimization is a major challenge. To address it, the two major contrib...

Evolutionary Optimization of Computationally Expensive Problems via Surrogate Modeling

We present a parallel evolutionary optimization algorithm that leverages surrogate models for solving computationally expensive design problems with general constraints, on a limited computational budget. The essential backbone of our framework is an evolutionary algorithm coupled with a feasible sequential quadratic programming solver in the spirit of Lamarckian learning.We employ a trust-regi...

Multiple Objective Evolutionary Algorithms for Independent, Computationally Expensive Objective Evaluations

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'...