PYGENMET MANUAL

Introduction

This repository contains the documentation of pygenmet, a genetic algorithms package developed in Python.

This book includes a collection of functions and examples, written in Python, that shows how to use the PyGenMet package.

Rendering the manual

To compile or generate the documentation with Jupyter Book try this from console:

Clone the repository:

git clone https://github.com/imarranz/pygenmet-manual.git

Render the manual with JupyterBook:

jupyter-book build pygenmet-manual/

Citation

I publish each release of PyGenMet on Zenodo and here is a list of version:

Version	Date	DOI
v1.0.0	August 22, 2021

If you'd like to cite this package, instead of a specific version, use the following DOI: https://doi.org/10.5281/zenodo.5233539. Here is the bibtex entry for the book:

@software{ibon_martinez_arranz_2021_5233539,
  author       = {Ibon Martínez-Arranz},
  title        = {imarranz/pygenmet: v1.0.0},
  month        = aug,
  year         = 2021,
  publisher    = {Zenodo},
  version      = {v1.0.0},
  doi          = {10.5281/zenodo.5233539},
  url          = {https://doi.org/10.5281/zenodo.5233539}
}

Last update on the website: www.imarranz.com/pygenmet-manual/

The following flowchart has been generated with mermaid

Include diagrams in your markdown files with mermaid
Basic syntax
Mermaid Cheat Sheet

flowchart TB
ids((Start)) ==> A[Initial Population]
A[Initial Population] ==Evaluate all<br>chromosomes in the<br>population==> B[Fitness Evaluation]
B[Fitness Evaluation] ==> F{Stopping Criteria}
F{Stopping Criteria} ==NO==> id1[Genetic Operators]
F{Stopping Criteria} ==YES==> G[Best<br>Chromosome<br>Result]
C[Selection] ==> D[Crossover]
D[Crossover] ==> E[Mutation]
subgraph id1[Genetic Operators]
  direction TB
  C[Selection]
  D[Crossover]
  E[Mutation]
end
subgraph id2[Algorithm]
 id1[Genetic Operators]
 B[Fitness Evaluation]
 F{Stopping Criteria}
end
id1[GeneticOperators] ==> B[Fitness Evaluation]
G[Best<br>Chromosome<br>Result] ==> ide((End))
subgraph id3[Genetic Algorithm Schema]
 A[Initial Population]
 id2[Algorithm]
 G[Best<br>Chromosome<br>Result]
end

style id1 fill:#aaa,stroke-width:4px
style id2 fill:#777,stroke-width:4px

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