咨询与建议

限定检索结果

文献类型

  • 9 篇 期刊文献
  • 6 篇 会议

馆藏范围

  • 15 篇 电子文献
  • 0 种 纸本馆藏

日期分布

学科分类号

  • 9 篇 理学
    • 9 篇 数学
    • 1 篇 生物学
    • 1 篇 统计学(可授理学、...
  • 8 篇 工学
    • 7 篇 计算机科学与技术...
    • 5 篇 软件工程
    • 2 篇 交通运输工程
    • 1 篇 机械工程
    • 1 篇 信息与通信工程
    • 1 篇 控制科学与工程
    • 1 篇 生物工程
    • 1 篇 安全科学与工程
  • 3 篇 管理学
    • 3 篇 管理科学与工程(可...
    • 2 篇 工商管理
  • 1 篇 法学
    • 1 篇 社会学

主题

  • 2 篇 optimization
  • 2 篇 benchmarking
  • 2 篇 multiobjective o...
  • 1 篇 jamming
  • 1 篇 performance gain
  • 1 篇 traveling salesp...
  • 1 篇 nonconvex square...
  • 1 篇 per-instance alg...
  • 1 篇 reinforcement le...
  • 1 篇 containers
  • 1 篇 self-supervised ...
  • 1 篇 channel impulse ...
  • 1 篇 5g mobile commun...
  • 1 篇 physical layer s...
  • 1 篇 proximal majoriz...
  • 1 篇 benchmark testin...
  • 1 篇 semismooth newto...
  • 1 篇 genetic algorith...
  • 1 篇 feature extracti...
  • 1 篇 low latency comm...

机构

  • 5 篇 big data analyti...
  • 3 篇 data science: st...
  • 3 篇 data management ...
  • 3 篇 big data analyti...
  • 2 篇 data science: st...
  • 1 篇 institut teknolo...
  • 1 篇 sorbonne univers...
  • 1 篇 scads.ai dresden...
  • 1 篇 statistics and o...
  • 1 篇 university of tw...
  • 1 篇 data science: st...
  • 1 篇 western australi...
  • 1 篇 chair of transpo...
  • 1 篇 school of comput...
  • 1 篇 school of mathem...
  • 1 篇 university cente...
  • 1 篇 national center ...
  • 1 篇 tu dresden big d...
  • 1 篇 department of co...
  • 1 篇 department of ap...

作者

  • 8 篇 kerschke pascal
  • 7 篇 trautmann heike
  • 5 篇 schäpermeier len...
  • 3 篇 prager raphael p...
  • 2 篇 heins jonathan
  • 2 篇 seiler moritz vi...
  • 2 篇 moritz vinzent s...
  • 2 篇 pascal kerschke
  • 2 篇 heike trautmann
  • 2 篇 grimme christian
  • 1 篇 jonathan heins
  • 1 篇 oliver ludger pr...
  • 1 篇 rodrigues agatha
  • 1 篇 nazaruddin yul y...
  • 1 篇 bossek jakob
  • 1 篇 de b. pereira ca...
  • 1 篇 felix rauschert
  • 1 篇 vinzent seiler m...
  • 1 篇 kim-chuan toh
  • 1 篇 peipei tang

语言

  • 15 篇 英文
检索条件"机构=Big Data Analytics in Transportation"
15 条 记 录,以下是1-10 订阅
排序:
Peak-A-Boo! Generating Multi-objective Multiple Peaks Benchmark Problems with Precise Pareto Sets  12th
Peak-A-Boo! Generating Multi-objective Multiple Peaks Benchm...
收藏 引用
12th International Conference on Evolutionary Multi-Criterion Optimization, EMO 2023
作者: Schäpermeier, Lennart Kerschke, Pascal Grimme, Christian Trautmann, Heike Big Data Analytics in Transportation TU Dresden & ScaDS.AI Dresden Germany Data Science: Statistics and Optimization University of Münster Münster Germany
The design and choice of benchmark suites are ongoing topics of discussion in the multi-objective optimization community. Some suites provide a good understanding of their Pareto sets and fronts, such as the well-know... 详细信息
来源: 评论
Reinvestigating the R2 Indicator: Achieving Pareto Compliance by Integration
arXiv
收藏 引用
arXiv 2024年
作者: Schäpermeier, Lennart Kerschke, Pascal Big Data Analytics in Transportation TU Dresden Germany & ScaDS.AI Dresden Leipzig Germany
In multi-objective optimization, set-based quality indicators are a cornerstone of benchmarking and performance assessment. They capture the quality of a set of trade-off solutions by reducing it to a scalar number. O... 详细信息
来源: 评论
Using Reinforcement Learning for Per-Instance Algorithm Configuration on the TSP
Using Reinforcement Learning for Per-Instance Algorithm Conf...
收藏 引用
2023 IEEE Symposium Series on Computational Intelligence, SSCI 2023
作者: Vinzent Seiler, Moritz Rook, Jeroen Heins, Jonathan Ludger Preub, Oliver Bossek, Jakob Trautmann, Heike University of Twente Data Management and Biometrics Enschede Netherlands Tu Dresden Big Data Analytics in Transportation Dresden Germany Aachen University Chair for Ai Methodology Rwth Aachen Germany University of Münster Data Science: Statistics and Optimization Münster Germany University of Twente Data Science: Statistics and Optimization Enschede Netherlands
Automated Algorithm Configuration (AAC) usually takes a global perspective: it identifies a parameter configuration for an (optimization) algorithm that maximizes a performance metric over a set of instances. However,... 详细信息
来源: 评论
Deep-ELA: Deep Exploratory Landscape Analysis with Self-Supervised Pretrained Transformers for Single- and Multi-Objective Continuous Optimization Problems
arXiv
收藏 引用
arXiv 2024年
作者: Seiler, Moritz Vinzent Kerschke, Pascal Trautmann, Heike Paderborn University Germany Big Data Analytics in Transportation TU Dresden Germany ScaDS.AI Dresden Leipzig Germany Data Management and Biometrics Group University of Twente Netherlands
In many recent works, the potential of Exploratory Landscape Analysis (ELA) features to numerically characterize, in particular, single-objective continuous optimization problems has been demonstrated. These numerical... 详细信息
来源: 评论
Dancing to the State of the Art? How Candidate Lists Influence LKH for Solving the Traveling Salesperson Problem
arXiv
收藏 引用
arXiv 2024年
作者: Heins, Jonathan Schäpermeier, Lennart Kerschke, Pascal Whitley, Darrell Big Data Analytics in Transportation TU Dresden Germany ScaDS.AI Dresden/Leipzig Dresden Germany Department of Computer Science Colorado State University Fort Collins United States
Solving the Traveling Salesperson Problem (TSP) remains a persistent challenge, despite its fundamental role in numerous generalized applications in modern contexts. Heuristic solvers address the demand for finding hi... 详细信息
来源: 评论
A COLLECTION OF DEEP LEARNING-BASED FEATURE-FREE APPROACHES FOR CHARACTERIZING SINGLE-OBJECTIVE CONTINUOUS FITNESS LANDSCAPES
arXiv
收藏 引用
arXiv 2022年
作者: Seiler, Moritz Vinzent Prager, Raphael Patrick Kerschke, Pascal Trautmann, Heike Data Science: Statistics and Optimization University of Münster Germany Big Data Analytics in Transportation Tu Dresden Germany University of Twente Netherlands
Exploratory Landscape Analysis is a powerful technique for numerically characterizing landscapes of single-objective continuous optimization problems. Landscape insights are crucial both for problem understanding as w... 详细信息
来源: 评论
MOLE: DIGGING TUNNELS THROUGH MULTIMODAL MULTI-OBJECTIVE LANDSCAPES
arXiv
收藏 引用
arXiv 2022年
作者: Schäpermeier, Lennart Grimme, Christian Kerschke, Pascal Big Data Analytics in Transportation TU Dresden Dresden Germany Statistics and Optimization Group University of Münster Münster Germany
Recent advances in the visualization of continuous multimodal multi-objective optimization (MMMOO) landscapes brought a new perspective to their search dynamics. Locally efficient (LE) sets, often considered as traps ... 详细信息
来源: 评论
Physical Layer Security: Learning-Aided Attack Detection based on 5G NR SRS
Physical Layer Security: Learning-Aided Attack Detection bas...
收藏 引用
IEEE Conference on Communications and Network Security (CNS)
作者: Jonas Ninnemann Felix Rauschert Paul Schwarzbach Pascal Kerschke Oliver Michler Chair of Transport Systems Information Technology Faculty of Transport and Traffic Sciences “Friedrich List” TUD Dresden University of Technology Chair of Big Data Analytics in Transportation Faculty of Transport and Traffic Sciences “Friedrich List” TUD Dresden University of Technology
Physical layer security (PLS) constitutes a crucial foundation for future trustworthy networks, particularly in scenarios involving low-resource devices, low-latency, and high-mobility applications. The effectiveness ... 详细信息
来源: 评论
Hybridizing Target- and SHAP-encoded Features for Algorithm Selection in Mixed-variable Black-box Optimization
arXiv
收藏 引用
arXiv 2024年
作者: Dietrich, Konstantin Prager, Raphael Patrick Doerr, Carola Trautmann, Heike Big Data Analytics in Transportation TU Dresden Germany ScaDS.AI Dresden Germany Data Science: Statistics and Optimization University of Münster Germany Sorbonne Université CNRS LIP6 Paris France Machine Learning and Optimisation Paderborn University Germany Data Management and Biometrics Group University of Twente Netherlands
Exploratory landscape analysis (ELA) is a well-established tool to characterize optimization problems via numerical features. ELA is used for problem comprehension, algorithm design, and applications such as automated... 详细信息
来源: 评论
Using Reinforcement Learning for Per-Instance Algorithm Configuration on the TSP
Using Reinforcement Learning for Per-Instance Algorithm Conf...
收藏 引用
IEEE Symposium Series on Computational Intelligence (SSCI)
作者: Moritz Vinzent Seiler Jeroen Rook Jonathan Heins Oliver Ludger Preuß Jakob Bossek Heike Trautmann Data Science: Statistics and Optimization University of Münster Münster Germany Data Management and Biometrics University of Twente Enschede Netherlands Big Data Analytics in Transportation TU Dresden Dresden Germany Chair for AI Methodology RWTH Aachen University Aachen Germany Data Science: Statistics and Optimization University of Twente Enschede Netherlands
Automated Algorithm Configuration (AAC) usually takes a global perspective: it identifies a parameter configuration for an (optimization) algorithm that maximizes a performance metric over a set of instances. However,...
来源: 评论