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Position: Leverage Foundational Models for Black-Box Optimization
International Conference on Machine LearningUndeniably, Large Language Models (LLMs) have stirred an extraordinary wave of innovation in the machine learning research domain, resulting in substantial impact across diverse fields such as reinforcement learning, robotics, and computer vision.
Xingyou Song +5 more
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Pretrained Optimization Model for Zero-Shot Black Box Optimization
Neural Information Processing SystemsZero-shot optimization involves optimizing a target task that was not seen during training, aiming to provide the optimal solution without or with minimal adjustments to the optimizer.
Xiaobin Li +5 more
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Robust Guided Diffusion for Offline Black-Box Optimization
Trans. Mach. Learn. Res.Offline black-box optimization aims to maximize a black-box function using an offline dataset of designs and their measured properties. Two main approaches have emerged: the forward approach, which learns a mapping from input to its value, thereby acting
Can Chen +4 more
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Features for Exploiting Black-Box Optimization Problem Structure
2013Black-box optimization BBO problems arise in numerous scientific and engineering applications and are characterized by computationally intensive objective functions, which severely limit the number of evaluations that can be performed. We present a robust set of features that analyze the fitness landscape of BBO problems and show how an algorithm ...
Abell, Tinus +4 more
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Black-Box solvers in combinatorial optimization
2015 International Conference on Industrial Engineering and Systems Management (IESM), 2015Black box optimizers have a long tradition in the field of operations research. These procedures treat the objective function evaluation as a black box and therefore do not take advantage of its specific structure. Black-box optimization refers to the process in which there is a complete separation between the evaluation of the objective function —and ...
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Black-Box Optimization in Railway Simulations
2020In railway timetabling one objective is that the timetable is robust against minor delays. One way to compute the robustness of a timetable is to simulate it with some predefined delays that occur and are propagated within the simulation. These simulations typically are complex and do not provide any information on the derivative of an objective ...
Julian Reisch, Natalia Kliewer
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Noisy multiobjective black-box optimization using bayesian optimization
Proceedings of the Genetic and Evolutionary Computation Conference Companion, 2019Expensive black-box problems are usually optimized by Bayesian Optimization (BO) since it can reduce evaluation costs via cheaper surrogates. The most popular model used in Bayesian Optimization is the Gaussian process (GP) whose posterior is based on a joint GP prior built by initial observations, so the posterior is also a Gaussian process ...
Hongyan Wang +4 more
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Black-box Optimization by Annealing Machines
The Brain & Neural Networks, 2022Shu Tanaka, Masashi Yamashita, Yuya Seki
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General Limits in Black-Box Optimization
2012We already observed that evolutionary algorithms are usually thought of as general problem solvers. This implies that they are designed according to a general idea of how search should be implemented. In the case of evolutionary algorithms this idea stems from an understanding of natural evolution.
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Diff-BBO: Diffusion-Based Inverse Modeling for Black-Box Optimization
arXiv.orgD. Wu +4 more
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