Application of a Genetic Algorithm to the "Infinite monkey theorem"
According to the MathWorks website (https://www.mathworks.com/help/gads/what-is-the-genetic-algorithm.html),
The genetic algorithm is a method for solving both constrained and unconstrained optimization problems that is based on natural selection, the process that drives biological evolution.
In the most basic sense, the Genetic Algorithm is an algorithm that attempts to mimic the idea of biological evolution in order to solve optimization problems. More information on the specifics of the algorithm can be found in the documentation folder.
The Infinite Monkey Theorem is the idea that if you give a monkey infinite time in front of a typewriter, the monkey will eventually produce a specified work (the most common example is a work of Shakespeare).
The project essentially combines these two concepts in order to produce a specified string. The program is given a target string and starts the algorithm by producing random letters. It continually runs the algorithm, giving each string a fitness rating until a generation produces a string with a fitness of 1.0, meaning an exact match to the target has been found.
Before continuing, make sure you have the latest version of python installed.
To install, you must clone the repository to your PC using the following command in a CLI (Command line interface)
git clone https://github.com/f0lg0/geneticAlgorithm-PY.git directory
where directory is the file path where you want the installation to download on your computer.
To run, first navigate to the installation in the CLI using the command
cd directory
where directory is the file path where you previously installed the repository to your computer.
Then, type in the CLI
python3 main.py
which should start running the program.
If this does not work, try replacing "python3" with "python" or "py".
If it still does not work, double check your installation of Python.
If ran correctly, the output in the terminal should look something like this:
Generation # 1456 | Fittest individual fitness: 0.9411764705882353 Target phrase: genetic algorithm ========================================================== Individual # 0 : genetic algorithv | Fitness: 0.9411764705882353 Individual # 1 : genetic algorithv | Fitness: 0.9411764705882353 Individual # 2 : genetic algorithb | Fitness: 0.9411764705882353 Individual # 3 : genetic algorithv | Fitness: 0.9411764705882353 Individual # 4 : genetic algorithb | Fitness: 0.9411764705882353 Individual # 5 : genetic algorithv | Fitness: 0.9411764705882353 Individual # 6 : genetic algorithv | Fitness: 0.9411764705882353 Individual # 7 : genetic algorithv | Fitness: 0.9411764705882353
Note: This is not the entire output, just a small portion.
This particular example shows the algorithm nearing completion, as it is very close to getting
the target phrse "genetic algorithm".
This python code was written in a Linux environment, so it may not work with Windows.
Feel free to play with this code and to criticize my work by giving me advice. Just please don't be rude.
Resize your terminal window to a pretty big size, output is going to occupy a lot of space.
Daniel Shiffman and his amazing "The Nature of Code" (chapter 9) book!