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Bayesian optimization for computationally extensive probability distributions. [PDF]
PLoS ONE, 2018 An efficient method for finding a better maximizer of computationally extensive probability distributions is proposed on the basis of a Bayesian optimization technique.
Ryo Tamura, Koji Hukushima
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Generative Multiform Bayesian Optimization
IEEE Transactions on Cybernetics, 2023 Many real-world problems, such as airfoil design, involve optimizing a black-box expensive objective function over complex structured input space (e.g., discrete space or non-Euclidean space). By mapping the complex structured input space into a latent space of dozens of variables, a two-stage procedure labeled as generative model based optimization ...
Zhendong Guo +5 more
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An improved hyperparameter optimization framework for AutoML systems using evolutionary algorithms
Scientific Reports, 2023 For any machine learning model, finding the optimal hyperparameter setting has a direct and significant impact on the model’s performance. In this paper, we discuss different types of hyperparameter optimization techniques.
Amala Mary Vincent, P. Jidesh
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Tuning of Bayesian optimization for materials synthesis: simulation of the one-dimensional case
Science and Technology of Advanced Materials: Methods, 2022 Materials exploration requires the optimization of a multidimensional space including the chemical composition and synthesis parameters such as temperature and pressure.
Ryo Nakayama +8 more
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Enhanced machine learning tree classifiers for lithology identification using Bayesian optimization
Applied Computing and Geosciences, 2022 Lithology identification is a fundamental activity in oil and gas exploration. The application of artificial intelligence (AI) is currently being adopted as a state-of-the-art means of automating lithology identification.
Solomon Asante-Okyere +2 more
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Journal of Fluid Science and Technology, 2021 We attempt to optimize the control parameters of traveling wave-like wall deformation for turbulent friction drag reduction using the Bayesian optimization. The Bayesian optimization is an optimization method based on stochastic processes, and it is good
Yusuke NABAE, Koji FUKAGATA
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Entropy, 2023 In order to reduce the errors caused by the idealization of the conventional analytical model in the transient planar source (TPS) method, a finite element model that more closely represents the actual heat transfer process was constructed.
Hualin Ji +4 more
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Co-Learning Bayesian Optimization
IEEE Transactions on Cybernetics, 2022 Bayesian optimization (BO) is well known to be sample-efficient for solving black-box problems. However, the BO algorithms can sometimes get stuck in suboptimal solutions even with plenty of samples. Intrinsically, such suboptimal problem of BO can attribute to the poor surrogate accuracy of the trained Gaussian process (GP), particularly that in the ...
Zhendong Guo +3 more
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Hybrid algorithm of Bayesian optimization and evolutionary algorithm in crystal structure prediction
Science and Technology of Advanced Materials: Methods, 2022 We propose a highly efficient searching algorithm in crystal structure prediction. The searching algorithm is a hybrid of the evolutionary algorithm and Bayesian optimization. The evolutionary algorithm is widely used in crystal structure prediction, and
Tomoki Yamashita +4 more
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Combining Bayesian optimization and Lipschitz optimization [PDF]
Machine Learning, 2020 Bayesian optimization and Lipschitz optimization have developed alternative techniques for optimizing black-box functions. They each exploit a different form of prior about the function. In this work, we explore strategies to combine these techniques for better global optimization.
Mohamed Osama Ahmed +2 more
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