Automated Author ProfileLong, Fu Xing
0000-0003-4550-5777
Long, Fu Xing
Current S-Index
Sum of Dataset Indices for all datasets
Average Dataset Index per Dataset
Average Dataset Index per dataset
Total Datasets
Total datasets for this author
Average FAIR Score
Average FAIR Score per dataset
Total Citations
Total citations to the author's datasets
Total Mentions
Total mentions of the author's datasets
S-Index Interpretation
The S-Index (Sharing Index) is a comprehensive metric that represents the cumulative impact of all your datasets. It is calculated as the sum of Dataset Index scores across all your claimed datasets.
What it means:
- A higher S-index indicates greater overall impact of your datasets relative to typical datasets in their fields of research
- The S-Index grows as you add more datasets or as existing datasets gain more citations and mentions
- It provides a single number to track your research data impact over time
Current S-Index: 2.6 (sum of 4 datasets Dataset Index scores)
More information here.
S-Index Over Time
Cumulative Citations Over Time
Cumulative Mentions Over Time
Datasets
This repository contains the data and code for the paper "Challenges of ELA-based Function Evolution using Genetic
Programming" This repository consists of separated folders, which contain the following data: ## Code: This is the main code used to run the GP functions. The main executable is 'main_gp.py', which executes a single run of the GP system (based on the passed-in argument, which is an index from 0-71 in our experiments). The data for the BBOB functions are generated using the 'preliminary' folder and the 'get_ela_preliminary.py' file. ## Data_GP: This contains the full logs from each GP run, separated by target function and dimension. ## data_random_func: This contains the same kind of data but for the Random Function Generator. ## Reproducibility: This contains all code used to analyse and visualize the resulting data. The notebook is structured in the same way as the paper, separated by figure.
Authors
- Long, Fu Xing ;
- Vermetten, Diederick ;
- Kononova, Anna V. ;
- Kalkreuth, Roman ;
- Yang, Kaifeng ;
- Bäck, Thomas ;
- Van Stein, Niki
This repository contains the data and code for the paper "Challenges of ELA-based Function Evolution using Genetic
Programming" This repository consists of separated folders, which contain the following data: ## Code: This is the main code used to run the GP functions. The main executable is 'main_gp.py', which executes a single run of the GP system (based on the passed-in argument, which is an index from 0-71 in our experiments). The data for the BBOB functions are generated using the 'preliminary' folder and the 'get_ela_preliminary.py' file. ## Data_GP: This contains the full logs from each GP run, separated by target function and dimension. ## data_random_func: This contains the same kind of data but for the Random Function Generator. ## Reproducibility: This contains all code used to analyse and visualize the resulting data. The notebook is structured in the same way as the paper, separated by figure.
Authors
- Long, Fu Xing ;
- Vermetten, Diederick ;
- Kononova, Anna V. ;
- Kalkreuth, Roman ;
- Yang, Kaifeng ;
- Bäck, Thomas ;
- Van Stein, Niki
This repository contains the ELA features, performance data and scripts for data collection and visualization from the paper 'BBOB Instance Analysis: Landscape Properties and Algorithm Performance across Problem Instances'
Authors
- Long, Fu Xing ;
- Vermetten, Diederick ;
- van Stein, Bas ;
- Kononova, Anna V.
This repository contains the ELA features, performance data and scripts for data collection and visualization from the paper 'BBOB Instance Analysis: Landscape Properties and Algorithm Performance across Problem Instances'
Authors
- Long, Fu Xing ;
- Vermetten, Diederick ;
- van Stein, Bas ;
- Kononova, Anna V.