检索结果-内蒙古大学图书馆

A domain-transformed surrogate-assisted differential evolutionary algorithm for hyperparameter optimisation of satellite handover strategy

引用

INFORMATION SCIENCES 2025年 700卷

作者： Yang, Zhe Deng, Libao Li, Chunlei Qin, Yifan Zhang, Lili Harbin Inst Technol Sch Informat Sci & Engn Weihai 264209 Shandong Peoples R China Dublin City Univ Sch Comp Dublin D09 V209 Ireland

With the rapid advancement of low-Earth-orbit (LEO) satellite technology, satellite phone calls have become increasingly widespread. However, this progress introduces new challenges: the high velocity of LEO satellites necessitates frequent reconnections between terminals and satellites, which can adversely affect communication quality. Moreover, the frequent signal measurements required to maintain connectivity significantly increase the energy consumption of the terminals. To address these challenges, this paper proposes a terminal measurement and satelliteswitching strategy. The strategy aims to minimize energy consumption during switching while preserving a high level of user experience. The strategy is based on predictions of satellite cell visibility time and beam visibility time. To further optimise the hyperparameters of this method, we established a simulation-based hyperparameter optimisation model and designed a domaintransformed surrogate-assisted differential evolutionary algorithm (DT-SADE). The algorithm utilises a radial basis function (RBF) model as a surrogate for global search and a Kriging model for local search, and uses domain transformation to address discrepancies between the simulation and surrogate models. Experimental results demonstrate that the proposed method outperforms comparative algorithms across multiple performance metrics, and that hyperparameter optimisation further enhances its performance, highlighting the significance and effectiveness of hyperparameter optimisation.

关键词： Domain Transform Surrogate-Assisted Evolutionary Algorithm hyperparameter optimisation Satellite Handover Strategy

来源：评论

学校读者我要写书评

暂无评论

hyperparameter optimisation with Early Termination of Poor Performers 11

Hyperparameter Optimisation with Early Termination of Poor P...

引用

11th Computer Science and Electronic Engineering Conference (CEEC)

作者： Marinov, Dobromir Karapetyan, Daniel Univ Essex Comp Sci & Elect Engn Colchester Essex England

ISBN: (纸本)9781728129525

It is typical for a machine learning system to have numerous hyperparameters that affect its learning rate and prediction quality. Finding a good combination of the hyperparameters is, however, a challenging job. This is mainly because evaluation of each combination is extremely expensive computationally;indeed, training a machine learning system on real data with just a single combination of hyperparameters usually takes hours or even days. In this paper, we address this challenge by trying to predict the performance of the machine learning system with a given combination of hyperparameters without completing the expensive learning process. Instead, we terminate the training process at an early stage, collect the model performance data and use it to predict which of the combinations of hyperparameters is most promising. Our preliminary experiments show that such a prediction improves the performance of the commonly used random search approach.

关键词： hyperparameter optimisation machine learning random search

来源：评论

学校读者我要写书评

暂无评论

Population based training and federated learning frameworks for hyperparameter optimisation and ML unfairness using Ulimisana optimisation Algorithm

引用

INFORMATION SCIENCES 2022年 612卷 132-150页

作者： Maumela, Tshifhiwa Nelwamondo, Fulufhelo Marwala, Tshilidzi Univ Johannesburg Johannesburg South Africa Natl Res Fdn Pretoria South Africa

This paper introduces the Ulimisana optimisation Algorithm enabled Population Based Training (PBT-UOA) framework which allows for hyperparameters to be fine-tuned using a population based meta-heuristic algorithm at the same time as parameters are being optimised. Models are trained until near-convergence on the updated hyperparameters and the parameters of the best performing model are shared to warm start the other mod-els in the next hyperparameter tuning iteration. In the PBT-UOA, all models are trained using the same dataset. This framework performed better than the Bayesian optimisation algorithm. This paper also introduces the Ulimisana optimisation Algorithm enabled Federated Learning (FL-UOA) framework which is an extension of the PBT-UOA. This framework is introduced to address the challenges of scattered datasets and privacy that is presented by the increase in connected end-devices. The FL-UOA learns on local data in scattered end-devices without sending datasets to a central server. The training datasets in local end-devices are used to evaluate models trained in other end -devices. The performance metrics are used to update the Social Trust Network (STN) of the FL-UOA framework. The FL-UOA outperformed the classic Federated Learning frame-work. This STN updating technique was tested in Machine Learning (ML) Unfairness to see how well it functioned as a regularisation term. This was achieved by training different models on subsets that contained datasets representing only specific sensitive groups. Results showed that by updating the hyperparameters while learning the parameters on the dataset scattered across different devices, the FL-UOA, takes advantage of diversified learning and reduces the ML Unfairness for models trained on group specific datasets. (c) 2022 The Author(s). Published by Elsevier Inc. This is an open access article under the CC BY license (http://***/licenses/by/4.0/).

关键词： Artificial Intelligence Co-operative agents hyperparameter optimisation Ubuntu Ulimisana Letsema Population based training Federated learning Collaborative learning

来源：评论

学校读者我要写书评

暂无评论

Analysis of the hyperparameter optimisation of four machine learning satellite imagery classification methods

引用

COMPUTATIONAL GEOSCIENCES 2024年第3期28卷 551-571页

作者： Alonso-Sarria, Francisco Valdivieso-Ros, Carmen Gomariz-Castillo, Francisco Univ Murcia Dept Geog Campus Merced Murcia 30001 Murcia Spain Inst Univ Agua & Medio Ambiente Univ Murcia Campus Espinardo Murcia 30100 Murcia Spain

The classification of land use and land cover (LULC) from remotely sensed imagery in semi-arid Mediterranean areas is a challenging task due to the fragmentation of the landscape and the diversity of spatial patterns. Recently, the use of deep learning (DL) for image analysis has increased compared to commonly used machine learning (ML) methods. This paper compares the performance of four algorithms, Random Forest (RF), Support Vector Machine (SVM), Multilayer Perceptron (MLP) and Convolutional Network (CNN), using multi-source data, applying an exhaustive optimisation process of the hyperparameters. The usual approach in the optimisation process of a LULC classification model is to keep the best model in terms of accuracy without analysing the rest of the results. In this study, we have analysed such results, discovering noteworthy patterns in a space defined by the mean and standard deviation of the validation accuracy estimated in a 10-fold cross validation (CV). The point distributions in such a space do not appear to be completely random, but show clusters of points that facilitate the discovery of hyperparameter values that tend to increase the mean accuracy and decrease its standard deviation. RF is not the most accurate model, but it is the less sensitive to changes in hyperparameters. Neural Networks, tend to increase commission and omission errors of the less represented classes because their optimisation lead the model to learn better the most frequent classes. On the other hand, RF and MLP prediction layers are the most accurate from a general qualitative point of view.

关键词： Machine learning LULC Convolutional neuronal networks Random forest Support vector machines hyperparameter optimisation

来源：评论

学校读者我要写书评

暂无评论

Automated hyperspectral vegetation index derivation using a hyperparameter optimisation framework for high-throughput plant phenotyping

引用

NEW PHYTOLOGIST 2022年第6期233卷 2659-2670页

作者： Koh, Joshua C. O. Banerjee, Bikram P. Spangenberg, German Kant, Surya Agr Victoria Grains Innovat Pk110 Natimuk Rd Horsham Vic 3400 Australia Agr Victoria AgriBio Ctr AgriBiosci 5 Ring Rd Bundoora Vic 3083 Australia La Trobe Univ Sch Appl Syst Biol Bundoora Vic 3083 Australia

Hyperspectral vegetation indices (VIs) are widely deployed in agriculture remote sensing and plant phenotyping to estimate plant biophysical and biochemical traits. However, existing VIs consist mainly of simple two-band indices that limit the net performance and often do not generalise well for traits other than those for which they were originally designed. We present an automated hyperspectral vegetation index (AutoVI) system for the rapid generation of novel two- to six-band trait-specific indices in a streamlined process covering model selection, optimisation and evaluation, driven by the tree parzen estimator algorithm. Its performance was tested in generating novel indices to estimate chlorophyll and sugar contents in wheat. Results showed that AutoVI can rapidly generate complex novel VIs (at least a four-band index) that correlated strongly (R-2 > 0.8) with measured chlorophyll and sugar contents in wheat. Automated hyperspectral vegetation index-derived indices were used as features in simple and stepwise multiple linear regressions for chlorophyll and sugar content estimation, and outperformed the results achieved with the existing 47 VIs and those provided using partial least squares regression. The AutoVI system can deliver novel trait-specific VIs readily adoptable to high-throughput plant phenotyping platforms and should appeal to plant scientists and breeders. A graphical user interface for the AutoVI is provided here.

关键词： automated vegetation index development chlorophyll estimation high-throughput plant phenotyping hyperparameter optimisation hyperspectral vegetation indices sugar estimation wheat

来源：评论

学校读者我要写书评

暂无评论

Exploring public attention in the circular economy through topic modelling with twin hyperparameter optimisation

引用

Energy and AI 2024年第4期18卷 258-280页

作者： Junhao Song Yingfang Yuan Kaiwen Chang Bing Xu Jin Xuan Wei Pang School of Mathematical and Computer Sciences Heriot-Watt UniversityEdinburghUnited Kingdom Faculty of Engineering Imperial College LondonLondonUnited Kingdom Edinburgh Business School Heriot-Watt UniversityEdinburghUnited Kingdom Faculty of Engineering and Physical Sciences University of SurreySurreyUnited Kingdom School of Management Science and Engineering Nanjing University of Information Science and TechnologyNanjingChina

To advance the circular economy(CE),it is crucial to gain insights into the evolution of public attention,cognitive pathways related to circular products,and key public *** achieve these objectives,we collected data from diverse platforms,including Twitter,Reddit,and The Guardian,and utilised three topic models to analyse the *** the performance of topic modelling may vary depending on hyperparameter settings,we proposed a novel framework that integrates twin(single-and multi-objective)hyperparameter timisation op-for CE *** experiments were conducted to determine appropriate hyperparameters under different constraints,providing valuable insights into the correlations between CE and public *** findings reveal that economic implications of sustainability and circular practices,particularly around recyclable materials and environmentally sustainable technologies,remain a significant public *** related to sustainable development and environmental protection technologies are particularly prominent on The Guardian,while Twitter discussions are comparatively *** insights highlight the importance of targeted education programmes,business incentives adopt CE practices,and stringent waste management policies alongside improved recycling processes.

关键词： Circular economy Pulic attention Topic modelling Machine learning hyperparameter optimisation

来源：评论

学校读者我要写书评

暂无评论

Accelerating hyperparameter optimisation with PyCOMPSs 19

Accelerating Hyperparameter Optimisation with PyCOMPSs

引用

48th International Conference on Parallel Processing (ICPP)

作者： Njoroge Kahira, Albert Bautista Gomez, Leonardo Conejero, Javier Badia, Rosa M. Barcelona Supercomp Ctr Barcelona Spain

ISBN: (纸本)9781450371964

Machine Learning applications now span across multiple domains due to the increase in computational power of modern systems. There has been a recent surge in Machine Learning applications in High Performance Computing (HPC) in an attempt to speed up training. However, besides training, hyperparameters optimisation(HPO) is one of the most time consuming and resource intensive parts in a Machine Learning Workflow. Numerous algorithms and tools exist to accelerate the process of finding the right parameters for a model. Most of these tools do not utilize the parallelism provided by modern systems and are serial or limited to a single node. The few ones that are offer distributed execution require a serious amount of programming effort. There is, therefore, a need for a tool/scheme that can scale and leverage HPC infrastructures such as supercomputers, with minimum programmers effort and little or no overhead in performance. We present a HPO scheme built on top of PyCOMPSs, a programming model and runtime which aims to ease the development of parallel applications for distributed infrastructures. We show that PyCOMPSs is a powerful framework that can accelerate the process of hyperparameter optimisation across multiple devices and computing units. We also show that PyCOMPSs provides easy programmability, seamless distribution and scalability, key features missing in existing tools. Furthermore, we perform a detailed performance analysis showing different configurations to demonstrate the effectiveness our approach.

关键词： Machine Learning hyperparameter optimisation Distributed Computing HPC PyCOMPSs

来源：评论

学校读者我要写书评

暂无评论

A Genetic Algorithm with Tree-structured Mutation for hyperparameter optimisation of Graph Neural Networks

A Genetic Algorithm with Tree-structured Mutation for Hyperp...

引用

IEEE Congress on Evolutionary Computation (IEEE CEC)

作者： Yuan, Yingfang Wang, Wenjun Pang, Wei Heriot Watt Univ Sch Math & Comp Sci Edinburgh Midlothian Scotland

ISBN: (纸本)9781728183923

In recent years, graph neural networks (GNNs) have gained increasing attention, as they possess the excellent capability of processing graph-related problems. In practice, hyperparameter optimisation (HPO) is critical for GNNs to achieve satisfactory results, but this process is costly because the evaluations of different hyperparameter settings require excessively training many GNNs. Many approaches have been proposed for HPO, which aims to identify promising hyperparameters efficiently. In particular, the genetic algorithm (GA) for HPO has been explored, which treats GNNs as a black-box model, of which only the outputs can be observed given a set of hyperparameters. However, because GNN models are sophisticated and the evaluations of hyperparameters on GNNs are expensive, GA requires advanced techniques to balance the exploration and exploitation of the search and make the optimisation more effective given limited computational resources. Therefore, we proposed a tree-structured mutation strategy for GA to alleviate this issue. Meanwhile, we reviewed the recent HPO works, which gives room for the idea of tree-structure to develop, and we hope our approach can further improve these HPO methods in the future.

关键词： generic algorithm tree-structured mutation graph neural network hyperparameter optimisation

来源：评论

学校读者我要写书评

暂无评论

A Systematic Comparison Study on hyperparameter optimisation of Graph Neural Networks for Molecular Property Prediction

A Systematic Comparison Study on Hyperparameter Optimisation...

引用

2nd Genetic and Evolutionary Computation Conference (GECCO)

作者： Yuan, Yingfang Wang, Wenjun Pang, Wei Heriot Watt Univ Sch Math & Comp Sci Edinburgh Midlothian Scotland

ISBN: (纸本)9781450383509

Graph neural networks (GNNs) have been proposed for a wide range of graph-related learning tasks. In particular, in recent years, an increasing number of GNN systems were applied to predict molecular properties. However, a direct impediment is to select appropriate hyperparameters to achieve satisfactory performance with lower computational cost. Meanwhile, many molecular datasets are far smaller than many other datasets in typical deep learning applications. Most hyperparameter optimization (HPO) methods have not been explored in terms of their efficiencies on such small datasets in the molecular domain. In this paper, we conducted a theoretical analysis of common and specific features for two stateof-the-art and popular algorithms for HPO: TPE and CMA-ES, and we compared them with random search (RS), which is used as a baseline. Experimental studies are carried out on several benchmarks in MoleculeNet, from different perspectives to investigate the impact of RS, TPE, and CMA-ES on HPO of GNNs for molecular property prediction. In our experiments, we concluded that RS, TPE, and CMA-ES have their individual advantages in tackling different specific molecular problems. Finally, we believe our work will motivate further research on GNN as applied to molecular machine learning problems in chemistry and materials sciences.

关键词： Graph Neural Networks Molecular Property Prediction hyperparameter optimisation

来源：评论

学校读者我要写书评

暂无评论

HOAX: a hyperparameter optimisation algorithm explorer for neural networks

引用

MOLECULAR PHYSICS 2023年第9-10期121卷

作者： Thie, Albert Menger, Maximilian F. S. J. Faraji, Shirin Univ Groningen Zernike Inst Adv Mat Fac Sci & Engn Groningen Netherlands Univ Groningen Zernike Inst Adv Mat Fac Sci & Engn Nijenborgh 4 NL-9747 AG Groningen Netherlands

Computational chemistry has become an important tool to predict and understand molecular properties and reactions. Even though recent years have seen a significant growth in new algorithms and computational methods that speed up quantum chemical calculations, the bottleneck for trajectory-based methods to study photo-induced processes is still the huge number of electronic structure calculations. In this work, we present an innovative solution, in which the amount of electronic structure calculations is drastically reduced, by employing machine learning algorithms and methods borrowed from the realm of artificial intelligence. However, applying these algorithms effectively requires finding optimal hyperparameters, which remains a challenge itself. Here we present an automated user-friendly framework, HOAX, to perform the hyperparameter optimisation for neural networks, which bypasses the need for a lengthy manual process. The neural network-generated potential energy surfaces (PESs) reduce the computational costs compared to the ab initio-based PESs. We perform a comparative investigation on the performance of different hyperparameter optimiziation algorithms, namely grid search, simulated annealing, genetic algorithm, and Bayesian optimizer in finding the optimal hyperparameters necessary for constructing the well-performing neural network in order to fit the PESs of small organic molecules. Our results show that this automated toolkit not only facilitate a straightforward way to perform the hyperparameter optimisation but also the resulting neural networks-based generated PESs are in reasonable agreement with the ab initio-based PESs.

关键词： Quantum chemistry machine learning neural networks hyperparameter optimisation

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：