Comparison between Multi-Population Migration and Random Immigrants Algorithms

This project was made for the Evolutionary Computation class of the Informatics Engineering Master of the University of Coimbra

Authors

• Jessica Cunha, 2016239495, jessicac@student.dei.uc.pt
• Ricardo Paiva, 2016253100, rjpaiva@student.dei.uc.pt

Requirements

• python3
  • necessary libraries: random, copy, math, json, os, pandas, numpy, matplotlib, scipy,
• R

FAQ

1 - How to run the code

1 - Choose the parameters in the file parameters.py that you want to test

2 - Run the file main.py (see 2.1 if help is needed), and you'll see the best individual of each generation printed in the terminal. If you didn't changed the code, a folder will be created with the best individual of each generation and inside, a log folder with the fitness of all individuals of each generation. To avoid this folder of being generated see section 4

2.1 - You can run the code by using an IDE (suggestion: PyCharm) or, if you're using Linux or MacOS, the terminal, by running:

python3 main.py

2 - How to generate the plots

For this you need to have R installed on your computer or the IDE RStudio. 1 - Open the file plot_generators.r

2 - Change the directory, to the folder where you have the files best.txt (this will be generated when running the code in 1)

3 - Run the file by using RStudio or in the terminal:

Rscript plot_generator.r

3 - How to do the statistical tests

1 - Open the file stat_alunos.py

2 - Change the code to load the data from the folder where your tests are. Note: The step 2 needs to be done first.

3 - Run the file by using an IDE or running in the terminal:

python3 stat_alunos.py

4 - I don't want to have the log folder with the fitness with all individuals

1 - Comment the lines 297 and 300-301 of the file main.py