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easyga

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.

https://github.com/danielwilczak101/easyga

Science Score: 44.0%

This score indicates how likely this project is to be science-related based on various indicators:

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    Found CITATION.cff file
  • ✓
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    Found codemeta.json file
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    Found .zenodo.json file
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    Scientific vocabulary similarity
    Low similarity (8.4%) to scientific vocabulary

Keywords

genetic-algorithms python python3
Last synced: 4 months ago · JSON representation ·

Repository

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.

Basic Info
  • Host: GitHub
  • Owner: danielwilczak101
  • License: mit
  • Language: Python
  • Default Branch: version1
  • Homepage:
  • Size: 2.96 MB
Statistics
  • Stars: 49
  • Watchers: 6
  • Forks: 7
  • Open Issues: 3
  • Releases: 3
Topics
genetic-algorithms python python3
Created over 5 years ago · Last pushed over 2 years ago
Metadata Files
Readme License Citation

README.md

EasyGA - Genetic Algorithms made Easy

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.

Check out our Wiki or Youtube for more information.

Installation:

Run python's pip3 to install:

Python pip3 install EasyGA

Getting started with EasyGA(Basic Example):

The goal of the basic example is to get all 5's in the chromosome. ```Python import EasyGA

Create the Genetic algorithm

ga = EasyGA.GA()

Evolve the whole genetic algorithm until termination has been reached

ga.evolve()

Print out the current generation and the population

ga.printgeneration() ga.printpopulation() ```

Output:

bash Current Generation : 15 Current population: Chromosome - 0 [7][4][4][5][3][5][5][8][3][7] / Fitness = 3 Chromosome - 1 [7][4][4][5][3][5][5][8][3][7] / Fitness = 3 Chromosome - 2 [7][4][4][5][3][5][5][8][3][7] / Fitness = 3 Chromosome - 3 [7][4][4][5][3][5][5][8][3][7] / Fitness = 3 Chromosome - 4 [7][2][4][5][3][5][5][8][3][7] / Fitness = 3 Chromosome - 5 [7][2][4][5][3][5][5][8][3][7] / Fitness = 3 Chromosome - 6 [5][8][8][6][10][10][5][7][2][7] / Fitness = 2 Chromosome - 7 [5][8][8][6][10][10][5][7][2][7] / Fitness = 2 Chromosome - 8 [5][8][8][6][10][10][5][7][2][7] / Fitness = 2 Chromosome - 9 [7][2][8][10][3][5][5][8][1][7] / Fitness = 2

Getting started with EasyGA (Password Cracker Example):

```Python import EasyGA import random

ga = EasyGA.GA()

word = input("Please enter a word: \n")

Basic Attributes

ga.chromosomelength = len(word) ga.fitnessgoal = len(word)

Size Attributes

ga.populationsize = 50 ga.generationgoal = 10000

User definded fitness

def password_fitness(chromosome):

return sum(1 for gene, letter
    in zip(chromosome, word)
    if gene.value == letter
)

ga.fitnessfunctionimpl = password_fitness

What the genes will look like.

ga.gene_impl = lambda: random.choice(["A","a","B","b","C","c","D","d","E","e", "F","f","G","g","H","h","I","i","J","j", "K","k","L","l","M","m","N","n","O","o", "P","p","Q","q","R","r","S","s","T","t", "U","u","V","v","W","w","X","x","Y","y", "Z","z"," "])

Evolve the gentic algorithm

ga.evolve()

Print out the current generation and the population

ga.printgeneration() ga.printpopulation()

Show graph of progress

ga.graph.highestvaluechromosome() ga.graph.show() ```

Ouput:

Please enter a word: EasyGA Current Generation : 44 Chromosome - 0 [E][a][s][y][G][A] / Fitness = 6 Chromosome - 1 [E][a][s][Y][G][A] / Fitness = 5 Chromosome - 2 [E][a][s][O][G][A] / Fitness = 5 Chromosome - 3 [E][a][s][Y][G][A] / Fitness = 5 Chromosome - 4 [E][a][s][c][G][A] / Fitness = 5 Chromosome - 5 [E][a][s][c][G][A] / Fitness = 5 Chromosome - 6 [E][a][s][y][Z][A] / Fitness = 5 Chromosome - 7 [E][a][s][Y][G][A] / Fitness = 5 Chromosome - 8 [E][a][s][y][Z][A] / Fitness = 5 Chromosome - 9 [E][a][s][Y][G][A] / Fitness = 5

Issues

We would love to know if your having any issues. Please start a new issue on the Issues Page.

Local System Approach

Download the repository to some folder on your computer.

https://github.com/danielwilczak101/EasyGA/archive/master.zip Use the run.py file inside the EasyGA folder to run your code. This is a local version of the package.

Check out our wiki for more information.

Owner

  • Name: Daniel Wilczak
  • Login: danielwilczak101
  • Kind: user
  • Location: Florida
  • Company: Embry Riddle EPPL - Research Lab

Love working with anything software. Experienced in Frontend, Backend and A.I Engineering. Working with a variety of languages, frameworks and platforms.

Citation (CITATION.cff) 

cff-version: 1.2.0
message: "If you use this software, please cite it as below."
authors:
  - family-names: Wilczak
    given-names: Daniel
  - family-names: Nguyen
    given-names: Jack
title: "EasyGA - Genetic Algorithms made Easy"
version: 2.0.4
date-released: 2021-13-05

GitHub Events

Total
  • Watch event: 1
Last Year
  • Watch event: 1

Committers

Last synced: almost 3 years ago

All Time
  • Total Commits: 645
  • Total Committers: 8
  • Avg Commits per committer: 80.625
  • Development Distribution Score (DDS): 0.547
Top Committers
Name Email Commits
SimpleArt 7****t@u****m 292
danielwilczak101 4****1@u****m 280
Daniel Wilczak d****1@g****m 42
RyleyGG g****0@g****m 23
jcurtis664 7****4@u****m 3
EREPPLab 6****b@u****m 3
Dylan Ballback 4****k@u****m 1
Ryley 3****G@u****m 1

Issues and Pull Requests

Last synced: 4 months ago

All Time
  • Total issues: 13
  • Total pull requests: 12
  • Average time to close issues: 28 days
  • Average time to close pull requests: 3 minutes
  • Total issue authors: 9
  • Total pull request authors: 2
  • Average comments per issue: 2.15
  • Average comments per pull request: 0.0
  • Merged pull requests: 11
  • Bot issues: 0
  • Bot pull requests: 0
Past Year
  • Issues: 0
  • Pull requests: 0
  • Average time to close issues: N/A
  • Average time to close pull requests: N/A
  • Issue authors: 0
  • Pull request authors: 0
  • Average comments per issue: 0
  • Average comments per pull request: 0
  • Merged pull requests: 0
  • Bot issues: 0
  • Bot pull requests: 0
View more stats
Top Authors
Issue Authors
  • danielwilczak101 (5)
  • drmichaelscherger (1)
  • BreakTechEnergy (1)
  • maryamsafiyah-tech (1)
  • AGZain (1)
  • fwollatz (1)
  • deetungsten (1)
  • stempelo (1)
  • raulazo-m (1)
Pull Request Authors
  • danielwilczak101 (11)
  • RyleyGG (1)
Top Labels
Issue Labels
Pull Request Labels

Packages

  • Total packages: 2
  • Total downloads:
    • pypi 556 last-month
  • Total dependent packages: 0
    (may contain duplicates)
  • Total dependent repositories: 2
    (may contain duplicates)
  • Total versions: 50
  • Total maintainers: 3
pypi.org: easyga

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.

  • Versions: 49
  • Dependent Packages: 0
  • Dependent Repositories: 1
  • Downloads: 547 Last month
Rankings
Stargazers count: 9.9%
Dependent packages count: 10.1%
Forks count: 12.6%
Average: 15.4%
Dependent repos count: 21.6%
Downloads: 22.9%
Maintainers (2)
panda1200 Mellowman
Last synced: 4 months ago
pypi.org: jaredtest1

Say hello!

  • Versions: 1
  • Dependent Packages: 0
  • Dependent Repositories: 1
  • Downloads: 9 Last month
Rankings
Stargazers count: 9.9%
Dependent packages count: 10.1%
Forks count: 12.6%
Dependent repos count: 21.6%
Average: 25.0%
Downloads: 70.8%
Maintainers (1)
jcurtis664
Last synced: 4 months ago

Dependencies

setup.py pypi
  • matplotlib *
  • pyserial *
  • pytest >=3.7
  • tabulate *