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. Those limitations might seriously impede their successful application. We present here an approach that makes use of the extra information provided by a Gaussian processes (GP) approximation model to guide the crucial model update step. We present here the advantages of using GP over other neural-net biologically inspired approaches. Results are presented for a real world-engineering problem involving the structural optimization of a satellite boom.