Evolutionary Optimization of Computationally Expensive Problems via Surrogate Modeling

@article{Ong2003EvolutionaryOO,
  title={Evolutionary Optimization of Computationally Expensive Problems via Surrogate Modeling},
  author={Yew Soon Ong and Prasanth B. Nair and Andy J. Keane},
  journal={AIAA Journal},
  year={2003},
  volume={41},
  pages={687-696}
}
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… 
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