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Generalizing Surrogate-Assisted Evolutionary Computation

<12>Surrogates are computationally efficient models for quality estimation in evolutionary computation. This paper proposed a framework for surrogate-assisted evolutionary algorithms (SAEAs) that can take advantage of estimation errors of surrogates. This provides a new perspective on using surrogates and offers a different avenue to accelerate evolution. For contributions to SAEAs including this work, I delivered an invited tutorial and a Keynote at international conferences, and wrote an invited Elsevier Journal survey paper. Invited by the President of International Operations Research Society, I gave a course on SAEAs in the 2012 Jyväskylä Summer School (Finland). 93 Google Scholar citations (22/10/13).