Single- and Multi-objective Evolutionary Optimization Assisted by Gaussian Random Field Metamodels

This paper presents and analyzes in detail an efficient search method based on Evolutionary Algorithms (EA) assisted by local Gaussian Random Field Metamodels (GRFM). It is created for the use in optimization problems with computationally expensive evaluation function(s). The role of GRFM is to predict objective function values for new candidate solutions by exploiting information recorded during previous evaluations. Moreover, GRFM are able to provide estimates of the confidence of their predictions