Abstract
This chapter introduces the definition of and motivations behind data-driven optimization. Two basic data-driven optimization paradigms, offline and online data-driven optimization, are introduced. A variety of heuristic population and individual based surrogate management strategies for surrogate assisted evolutionary optimization are presented, and mathematically more established model management strategies such as the trust region method and acquisition functions, also known as infill criteria, are introduced. An approach to surrogate-assisted evolutionary search of robust optimal solutions is presented. Finally, performance indicators for assessing the quality of surrogates for guiding evolutionary optimization are given.
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Jin, Y., Wang, H., Sun, C. (2021). Data-Driven Surrogate-Assisted Evolutionary Optimization. In: Data-Driven Evolutionary Optimization. Studies in Computational Intelligence, vol 975. Springer, Cham. https://doi.org/10.1007/978-3-030-74640-7_5
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DOI: https://doi.org/10.1007/978-3-030-74640-7_5
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