135 lines
4.0 KiB
Markdown
135 lines
4.0 KiB
Markdown
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# EasyGA - Genetic Algorithms made Easy
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EasyGA is a python package designed to provide an easy-to-use Genetic Algorithm. The package is designed to work right out of the box, while also allowing the user to customize features as they see fit.
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## Check out our [Wiki](https://github.com/danielwilczak101/EasyGA/wiki) or [Youtube](https://www.youtube.com/watch?v=jbuDKwIiYBw) for more information.
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## Installation:
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Run python's pip3 to install:
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```Python
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pip3 install EasyGA
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```
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## Getting started with EasyGA(Basic Example):
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The goal of the basic example is to get all 5's in the chromosome.
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```Python
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import EasyGA
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# Create the Genetic algorithm
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ga = EasyGA.GA()
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# Evolve the whole genetic algorithm until termination has been reached
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ga.evolve()
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# Print out the current generation and the population
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ga.print_generation()
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ga.print_population()
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```
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### Output:
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```bash
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Current Generation : 15
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Current population:
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Chromosome - 0 [7][4][4][5][3][5][5][8][3][7] / Fitness = 3
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Chromosome - 1 [7][4][4][5][3][5][5][8][3][7] / Fitness = 3
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Chromosome - 2 [7][4][4][5][3][5][5][8][3][7] / Fitness = 3
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Chromosome - 3 [7][4][4][5][3][5][5][8][3][7] / Fitness = 3
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Chromosome - 4 [7][2][4][5][3][5][5][8][3][7] / Fitness = 3
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Chromosome - 5 [7][2][4][5][3][5][5][8][3][7] / Fitness = 3
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Chromosome - 6 [5][8][8][6][10][10][5][7][2][7] / Fitness = 2
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Chromosome - 7 [5][8][8][6][10][10][5][7][2][7] / Fitness = 2
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Chromosome - 8 [5][8][8][6][10][10][5][7][2][7] / Fitness = 2
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Chromosome - 9 [7][2][8][10][3][5][5][8][1][7] / Fitness = 2
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```
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## Getting started with EasyGA (Password Cracker Example):
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```Python
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import EasyGA
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import random
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ga = EasyGA.GA()
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word = input("Please enter a word: \n")
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# Basic Attributes
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ga.chromosome_length = len(word)
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ga.fitness_goal = len(word)
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# Size Attributes
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ga.population_size = 50
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ga.generation_goal = 10000
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# User definded fitness
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def password_fitness(chromosome):
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return sum(1 for gene, letter
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in zip(chromosome, word)
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if gene.value == letter
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)
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ga.fitness_function_impl = password_fitness
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# What the genes will look like.
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ga.gene_impl = lambda: random.choice(["A","a","B","b","C","c","D","d","E","e",
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"F","f","G","g","H","h","I","i","J","j",
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"K","k","L","l","M","m","N","n","O","o",
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"P","p","Q","q","R","r","S","s","T","t",
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"U","u","V","v","W","w","X","x","Y","y",
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"Z","z"," "])
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# Evolve the gentic algorithm
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ga.evolve()
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# Print out the current generation and the population
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ga.print_generation()
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ga.print_population()
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# Show graph of progress
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ga.graph.highest_value_chromosome()
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ga.graph.show()
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```
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## Ouput:
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```
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Please enter a word:
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EasyGA
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Current Generation : 44
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Chromosome - 0 [E][a][s][y][G][A] / Fitness = 6
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Chromosome - 1 [E][a][s][Y][G][A] / Fitness = 5
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Chromosome - 2 [E][a][s][O][G][A] / Fitness = 5
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Chromosome - 3 [E][a][s][Y][G][A] / Fitness = 5
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Chromosome - 4 [E][a][s][c][G][A] / Fitness = 5
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Chromosome - 5 [E][a][s][c][G][A] / Fitness = 5
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Chromosome - 6 [E][a][s][y][Z][A] / Fitness = 5
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Chromosome - 7 [E][a][s][Y][G][A] / Fitness = 5
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Chromosome - 8 [E][a][s][y][Z][A] / Fitness = 5
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Chromosome - 9 [E][a][s][Y][G][A] / Fitness = 5
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```
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<img width="500px" src="https://raw.githubusercontent.com/danielwilczak101/EasyGA/media/images/password_cracker_results.png" />
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## Issues
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We would love to know if your having any issues. Please start a new issue on the [Issues Page](https://github.com/danielwilczak101/EasyGA/issues).
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## Local System Approach
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Download the repository to some folder on your computer.
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```
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https://github.com/danielwilczak101/EasyGA/archive/master.zip
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```
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Use the run.py file inside the EasyGA folder to run your code. This is a local version of the package.
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## Check out our [wiki](https://github.com/danielwilczak101/EasyGA/wiki) for more information.
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