An Unbounded Archive-Based Inverse Model in Evolutionary Multi-objective Optimization

The overview of the UAIM.

This paper has been accepted by 18th International Conference on Parallel Problem Solving from Nature (PPSN 2024). And This project is a pytorch implementation of An Unbounded Archive-Based Inverse Model in Evolutionary Multi-objective Optimization.

Installation

Requirements

Our provide the packages file of our environment (requirement.txt), you can using the following command to download the environment:

• pip install -r requirements.txt

Parameters

• pop_size: The population size of EAs.
• archive: Whether using UARM (unbounded archive) (1: Using unbounded archive to train inverse model, 0: Do not use unbounded archive to train inverse model)
• mode: Whether using replacement mechanism (1/0)

Training

cd /projects/UAIM
python main.py --problem_name 'dtlz7' --archive 1 --mode 1 --pop_size 55