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Model Uncertainty in Evolutionary Optimization and Bayesian Optimization: A Comparative Analysis
March 22, 2024, 4:43 a.m. | Hao Hao, Xiaoqun Zhang, Aimin Zhou
cs.LG updates on arXiv.org arxiv.org
Abstract: Black-box optimization problems, which are common in many real-world applications, require optimization through input-output interactions without access to internal workings. This often leads to significant computational resources being consumed for simulations. Bayesian Optimization (BO) and Surrogate-Assisted Evolutionary Algorithm (SAEA) are two widely used gradient-free optimization techniques employed to address such challenges. Both approaches follow a similar iterative procedure that relies on surrogate models to guide the search process. This paper aims to elucidate the similarities …
abstract algorithm analysis applications arxiv bayesian box comparative analysis computational cs.lg cs.ne free gradient input-output interactions leads optimization resources simulations through type uncertainty world
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