Evolutionary Optimization of Computationally Expensive Problems via Surrogate Modeling

Abstract

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-region approach for interleaving use of exact modelsfortheobjectiveandconstraintfunctionswithcomputationallycheapsurrogatemodelsduringlocalsearch. In contrastto earlier work, we construct local surrogatemodels using radial basis functionsmotivated by theprinciple of transductive inference. Further, the present approach retains the intrinsic parallelism of evolutionary algorithms and can hence be readily implemented on grid computing infrastructures. Experimental results are presented for some benchmark test functions and an aerodynamic wing design problem to demonstrate that our algorithm converges to good designs on a limited computational budget.