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IA - Genetic algorithms (LibGA)

In this project, the LibGA library developed by Arthur L. Corcoran has been implemented to find the max clique of some common instances. It is considered an introductory project to genetic algorithms and is not intended to be overly complex or precise.

Getting Started

These instructions will get you a copy of the project up and running on your local machine for development and testing purposes.

Prerequisites

What things you need to install the software and how to install them:

  • Nothing at all, everything you need is in this repository.

Usage example

The main configuration for LibGA is GAconfig and the rest is in the configs folder, which is where the bash scripts will take them from to change them automatically.

The available instances are in the instances folder, but you can add any other instance you want. Be sure to add them in the header of the config file for the program to use.

The code is divided into 3 different files:

  • GA Test: which is the normal program for using LibGA and uses the configuration of the GAconfig file. It can be used with its bash file to automate the change of configurations with those in the config folder and perform 10 tests per configuration file. A .csv file is created with all results and also the individual results of each simulation are saved in a .txt.

  • GA Test Report: modification of GA Test in which only the GAconfig configuration is used. The value of mu_rate is progressively changed, with constant x_rate, and after x_rate, keeping mu_rate constant. It is recommended to use the bash script with the same name to perform the 20 repetitions (50 samples of each) automatically. A .csv file is created with the final results.

  • GA Total Test: exactly the same process as in GA Test Report but a simulation is performed by varying mu_rate and x_rate at the same time. In such a way that a 20x20 matrix is obtained with the best solution for each pair of mu_rate and x_rate values, which is saved in a .csv file. Example below for the instance p_hat300_1.

Contributing

  1. Fork it (https://github.com/rsilverioo/IA_genetic_algo/fork)
  2. Create your feature branch (git checkout -b feature/fooBar)
  3. Commit your changes (git commit -am 'Add some fooBar')
  4. Push to the branch (git push origin feature/fooBar)
  5. Create a new Pull Request

Authors

License

This project is licensed under the MIT License - see the LICENSE file for details

Acknowledgments

  • Claudio Rossi - For minor changes in LibGA lib