An adaption of the Wong Evolutionary Algorithm (WEA) for the maximum clique problem. It results to be a simple genetic algorithm based on feasibility maintenance.
- Download from the repository
- See prerequisites
- Follow the installation
- See running
- Official Python (CPython == 3.7, MUST for the snap-stanford package) and PIP
- OS: Linux, Mac, Windows
Install the dependencies with pip
pip install -r requirements.txt
Optionally download any .edges or .mtx dataset:
- Download the dataset form http://snap.stanford.edu/data/index.html or https://networkrepository.com/dimacs.php
- Extract and copy the dataset to
data/input
Add additional notes about how to deploy this on a live system
python main.py
Using a downloaded dataset:
python main.py --dataset=facebook/0.edges
Note: It's already indexed to data/input. You may need to change the WEAClique settings.
| Option | Description |
|---|---|
| Genotype | List of nodes of dynamic size. This forms a feasible clique. |
| Crossover | Modified uniform crossover for feasible maintenance. |
| Mutation | Modified random resetting for feasible maintenance. |
| Life time adaption | Lamarckian Model. |
| Hill-Climbing | Stochastic local search. |
| Parent selection | Tournament selection. |
| Populational replacement | Generational replacement model. |
| Stop condition | Unchanged fitness and maximum iteration based. |
| Fitness function | Clique size. |
Uncompared