genetics's Introduction

DISCLAIMER

This project is only for personal challenge and educational purpose, no other pretention than those ones.
I could have used some libs such as pyevolve but the goal was more to understand how it works, what I am doing and why I am doing it.
Objective was also to improve my personal skills in Python but perhaps sometimes my choices/approaches are not so 'pythonic', sorry about that.

OVERVIEW

Goal of this project is to solve problems by using genetic algorithms.
So far the problems to solve are:

sudoku
some other to come later (maybe)

GA pseudo-algorithm used

generate initial population
repeat
    rank the solutions, and retain only the percentage specified by selection rate
    repeat
        randomly select two solutions from the population
        randomly choose a crossover point
        recombine the solutions to produce n new solutions
        apply the mutation operator to the solutions
    until a new population has been produced
until a solution is found or the maximum number of generations is reached

TECHNICAL PART

Dependencies & Installation

Basically, this project requires Python 3.7 in addition to numpy package.

Directory & code structure

Here is the structure of the project:

    project
      |__ objects    (contains classes)
      |__ samples    (contains .txt files to load)
      |__ sudoku     (utility functions to work with GA and sudokus)
      |__ tests      (unit and integration testing)
      |__ utils      (helper functions for the whole program)
      main.py        (the main script to launch)

Run the app on your local computer

Run the following commands in the project's root directory: python main.py --population-size 10000 --selection-rate 0.2 --random-selection-rate 0.2 --children 5 --mutation-rate 0.3 --max-generations 500 --pencil-mark True --model 4x4-beginner --restart-nb-generations 40

Of course, adapt the parameters to the values you want to set.

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