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pro vyhledávání: '"Rowe, Jonathan"'
Autor:
Aishwaryaprajna, Rowe, Jonathan E.
We present an empirical study of a range of evolutionary algorithms applied to various noisy combinatorial optimisation problems. There are three sets of experiments. The first looks at several toy problems, such as OneMax and other linear problems.
Externí odkaz:
http://arxiv.org/abs/2110.02288
We study the success probability for a variant of the secretary problem, with noisy observations and multiple offline selection. Our formulation emulates, and is motivated by, problems involving noisy selection arising in the disciplines of stochasti
Externí odkaz:
http://arxiv.org/abs/2106.01185
We introduce a sequential learning algorithm to address a robust controller tuning problem, which in effect, finds (with high probability) a candidate solution satisfying the internal performance constraint to a chance-constrained program which has b
Externí odkaz:
http://arxiv.org/abs/2102.09738
Autor:
Chin, Robert, Maass, Alejandro I., Ulapane, Nalika, Manzie, Chris, Shames, Iman, Nešić, Dragan, Rowe, Jonathan E., Nakada, Hayato
Active learning is proposed for selection of the next operating points in the design of experiments, for identifying linear parameter-varying systems. We extend existing approaches found in literature to multiple-input multiple-output systems with a
Externí odkaz:
http://arxiv.org/abs/2005.00711
We present results on the estimation and evaluation of success probabilities for ordinal optimisation over uncountable sets (such as subsets of $\mathbb{R}^{d}$). Our formulation invokes an assumption of a Gaussian copula model, and we show that the
Externí odkaz:
http://arxiv.org/abs/1911.01993
Akademický článek
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Autor:
Aishwaryaprajna, Rowe, Jonathan E.
Publikováno v:
In Theoretical Computer Science 12 May 2023 957