A data-driven evolutionary transfer optimization for expensive problems in dynamic environments Ke Li*, Renzhi Chen*, Xin Yao* [Paper] [Supplementary]
This repository contains Python implementation of the algorithm framework for Batched Data-Driven Evolutionary Multi-Objective Optimization Based on Manifold Interpolation.
algorithms/ --- algorithms definitions
problems/ --- multi-objective problem definitions
revision/ -- patch for Gpy package
scripts/ --- scripts for batch experiments
├── build.sh --- complie the c lib for test problems
├── run.sh -- run the experiment
main.py --- main execution file
- Python version: tested in Python 3.7.7
- Operating system: tested in Ubuntu 20.04
Run the main file with python with specified arguments:
python3.7 main.py --problem Movingpeak --n-var 6
Run the script file with bash, for example:
./run.sh
The optimization results are saved in txt format. They are stored under the folder:
output/data/{problem}/x{n}y{m}/{algo}-{exp-name}/{seed}/
If you find our repository helpful to your research, please cite our paper:
@article{KeLi2023,
author={Li, Ke and Chen, Renzhi and Yao, Xin},
journal={IEEE Transactions on Evolutionary Computation},
title={A Data-Driven Evolutionary Transfer Optimization for Expensive Problems in Dynamic Environments},
year={2023},
volume={},
number={},
pages={1-1},
doi={10.1109/TEVC.2023.3307244}}