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A simple genetic algorithm for the maximum clique problem based on feasibility maintenance.

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WEA-Clique

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.

Getting Started

  • Download from the repository
  • See prerequisites
  • Follow the installation
  • See running

Prerequisites

  • Official Python (CPython == 3.7, MUST for the snap-stanford package) and PIP
  • OS: Linux, Mac, Windows

Installation

Install the dependencies with pip

pip install -r requirements.txt

Downloading a dataset

Optionally download any .edges or .mtx dataset:

Running

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.

Algorithm 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.

Performance

Uncompared

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A simple genetic algorithm for the maximum clique problem based on feasibility maintenance.

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