Black-box optimizationLearning to optimizeMeta-learningRecurrent neural networksConstrained optimizationRecently, neural networks trained as optimizers under the "learning to learn" or meta-learning framework have been shown to be effective for a broad range of optimization tasks including derivative-free ...
MetaBox: A Benchmark Platform for Meta-Black-Box Optimization with Reinforcement Learning (https://arxiv.org/abs/2310.08252) gmc-drl.github.io/MetaBox/ Resources Readme License BSD-3-Clause license Activity Stars 0 stars Watchers 0 watching Forks 0 forks Report repository Releases ...
- Machine learning for automatic algorithm selection and configuration - Meta-learning - Transfer of approaches between machine learning and optimization - Taxonomies/ontologies for describing the algorithm instance space - Complementary analysis of different benchmarking datasets - Any other topic relating r...
A. Combining radial basis function surrogates and dynamic coordinate search in high-dimensional expensive black-box optimization. Engineering Optimization 45, 529–555 (2013). Article Google Scholar Eriksson, D., Bindel, D. & Shoemaker, C. A. pySOT: Python surrogate optimization toolbox. https:...
The other researchers are free to run these black-box models with their optimization algorithm of choice for an effective comparative analysis. The world of optimization through meta-heuristics is an excellent choice for the optimization of EVs as these problem formulations require a lot of ...
Unser. Monte-carlo sure: A black- box optimization of regularization parameters for general de- noising algorithms. IEEE Transacitons on Image Processing, 17(9):1540–1554, 2008. 3 [36] Jae Woong Soh, Sunwoo Cho, and Nam Ik Cho. Meta- transfer learning for zero-...
A variety of approaches have been proposed that vary based on how the adaptation portion of the training process is performed. These can broadly be classified into three categories: “black-box” or model-based, metric-based, and optimization-based approaches. ...
Symbolic Discovery of Optimization Algorithms 该研究提出将算法发现表述为程序搜索的方法,并将其应用于发现深度神经网络训练的优化算法,以此推出的新优化器 Lion。在广泛任务中,包括图像分类、视觉-语言对比学习、扩散模型和语言建模的结果表明, Lion 优于主流优化器(如 Adam 和 Adafactor)。例如,在扩散模型上,Lion ...
基于优化的方法 Optimization-Based 本文为系列文章第一篇,主要介绍Meta Learning是什么,以及基于度量的方法中最经典的孪生网络。 什么是Meta Learning Meta Learning,通常称为“learning to learn”,可以理解为掌握学习的方法。 普通的机器学习通常是让模型学习做一件具体的事,比如分辨车主是否需要导航;而Meta Learning想...
Surrogate Learning in Meta-Black-Box Optimization: A Preliminary Study Here we provide source codes of Surr-RLDE, which has been recently accepted by GECCO 2025. Citation The PDF version of the paper is available here. If you find our Surr-RLDE useful, please cite it in your publications ...