mabbob_ela_as_5d_regression_DiagonalCMA

OpenML2024-07-10 更新2025-12-20 收录

黑盒优化算法性能预测

探索性景观分析

数据链接：

https://www.openml.org/search?type=data&sort=runs&status=active&id=46271 数据链接链接失效反馈

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资源简介：

Algorithm performane prediction problem on 1120 5d MA-BBOB test problems using ELA features to learn which of five algorithms has the highest AUCC.

创建时间：

2024-07-10

ELA Bootstrapped Feature Values

探索性景观分析

统计学习

Bootstrapped values for 33 ELA features:- FDC- Dispersion [7 Values]- Information significance [First, Second and N orders]- Information Content [Using both NN and Random sorting]- Linear and Quadrati

DataCite Commons2021-02-13 更新150

ELA Bootstrapped Feature Values

优化问题

探索性景观分析

Figshare2021-01-27 更新10

黑盒优化算法组合

探索性景观分析

# Reproducibility files for 'Similarity-based Portfolio Construction for Black-box Optimization' All files required for reproducing the results shown in the paper can be found in 'reproducibility.

Zenodo2026-02-03 更新00

PPSN_Based_探索性景观分析采样策略敏感性研究实验数据

探索性景观分析

采样优化

本数据集为研究“探索性景观分析对采样策略高度敏感”提供特征值数据，包含R包flacco中所有“低成本”特征值，基于5种采样策略（Random、Randu、LHS、iLHS、Sobol）在5维空间计算，同时提供分类结果、统计数据及可视化图表相关文件，共7个文件。研究“Exploratory Landscape Analysis is Strongly Sensitive to the Sampli

海数据60

Experimental Data Set for the study "Exploratory Landscape Analysis is Strongly Sensitive to the Sampling Strategy"

探索性景观分析

采样策略敏感性

This are the feature values used in the study "Exploratory Landscape Analysis is Strongly Sensitive to the Sampling Strategy". The dataset regroups feature values for every "cheap" features availab

NIAID Data Ecosystem10

mabbob_ela_as_5d_regression_DiagonalCMA

资源简介：

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