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Surrogate‐based methods for black‐box optimization [PDF]
International Transactions in Operational Research, 2016 AbstractIn this paper, we survey methods that are currently used in black‐box optimization, that is, the kind of problems whose objective functions are very expensive to evaluate and no analytical or derivative information is available. We concentrate on a particular family of methods, in which surrogate (or meta) models are iteratively constructed and
Vu, Ky Khac +3 more
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Optimization on Black Box Function Optimization Problem [PDF]
Mathematical Problems in Engineering, 2015 There are a large number of engineering optimization problems in real world, whose input-output relationships are vague and indistinct. Here, they are called black box function optimization problem (BBFOP). Then, inspired by the mechanism of neuroendocrine system regulating immune system, BP neural network modified immune optimization algorithm (NN-MIA)
Jin-ke Xiao +3 more
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Black Box Optimization Using QUBO and the Cross Entropy Method [PDF]
International Conference on Conceptual Structures, 2022 Black-box optimization (BBO) can be used to optimize functions whose analytic form is unknown. A common approach to realising BBO is to learn a surrogate model which approximates the target black-box function which can then be solved via white-box ...
Jonas Nusslein +4 more
semanticscholar +1 more source
Black-box optimization methods for hypersonic flow problems [PDF]
International Journal of Fluid EngineeringThis review examines the application of black-box optimization methods to hypersonic flow problems, with a particular emphasis on scenarios where computational fluid dynamics (CFD) simulations function as opaque, nontransparent models.
Davood Hoseinzade +2 more
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Black Box Optimization Benchmarking of the GLOBAL Method [PDF]
Evolutionary Computation, 2012 GLOBAL is a multi-start type stochastic method for bound constrained global optimization problems. Its goal is to find the best local minima that are potentially global. For this reason it involves a combination of sampling, clustering, and local search.
Pál, Lászlo +3 more
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Chemical Engineering Transactions, 2021 Rigorous black-box simulations are useful to describe complex systems. However, it cannot be directly integrated into mathematical programming models in some algebraic modeling environments because of the lack of symbolic formulation.
Lucas F. Santos +3 more
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ACM SIGEVOlution, 2021 Nevergrad is an open source platform for black-box optimization. Join the user group! And if you like Nevergrad, please support us by adding a star on GitHub (https://github.com/facebookresearch/nevergrad).
Bennet, Pauline +5 more
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Attacking Black-Box Image Classifiers With Particle Swarm Optimization
IEEE Access, 2019 In order to better solve the shortcomings of Deep Neural Networks (DNNs) susceptible to adversarial examples, evaluating existing neural network classification performance and increasing training sets to improve the robustness of classification models ...
Quanxin Zhang +3 more
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Science and Technology of Advanced Materials: Methods, 2023 Materials screening by high-throughput first-principles calculations is a powerful tool for exploring novel materials with preferable properties. Machine learning techniques are expected to accelerate materials screening by constructing surrogate models ...
Akira Takahashi +4 more
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Design Optimization of Noise Filter Using Quantum Annealer
IEEE Access, 2023 The use of quantum annealers in black-box optimization to obtain the desired properties of a product with a small number of trials has attracted attention. However, the application of this technique to engineering design problems has been limited.
Akihisa Okada +5 more
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