# Genex
> Genex makes it easy to write Genetic Algorithms with Elixir.
[](https://travis-ci.org/seanmor5/genex)
[](https://coveralls.io/github/seanmor5/genex?branch=master)
[](https://hex.pm/packages/genex/0.1.4)
This library is inspired by Python's [DEAP](https://github.com/deap/deap).
## Documentation
Documentation is available at [https://hexdocs.pm/genex/introduction-overview.html](https://hexdocs.pm/genex/introduction-overview.html)
## Installation
The package can be installed by adding `genex` to your list of dependencies in `mix.exs`.
```elixir
def deps do
[
{:genex, "~> 0.1.0"}
]
end
```
## Usage
Genex requires an implementation module with 3 functions: `encoding/0`, `fitness_function/1`, and `terminate?/1`.
```elixir
defmodule OneMax do
use Genex
def encoding do
for _ <- 1..10, do: Enum.random(0..1)
end
def fitness_function(chromosome), do: Enum.sum(chromosome.genes)
def terminate?(population), do: population.max_fitness == 10
end
```
Now, run `iex -S mix`.
Then:
```
iex> OneMax.run()
```
## Features
Genex strives to be as simple and customizable as possible. Along with the ability to customize EVERY step of your Genetic algorithm, Genex comes with the following features:
- 6 Selection Operators
- 7 Crossover Operators
- 6 Mutation Operators
- Customizable Population Statistics
- Customizable Benchmarking of Algorithms
- Exportable Genealogy Tree
- Flexible Encoding of Chromosomes
- Simple Text Visualizations
## Benchmarks
To run benchmarks, clone this repo. In the `genex` directory run:
```
mix run bench/benchmarks.exs
```
You can also run the individual benchmarks available in the `bench/` directory. This will take some time!
## Contributing
If you have any problems with Genex, please open an issue! If you have a fix for an issue, submit a pull request.
## Roadmap
The next phase of this library will involve extensive performance improvements. Most of the algorithms involve processing very large lists. This is an ideal job for a NIF.
If anybody has any experience writing NIFs or writing algorithms for processing large lists, [email me](mailto:[email protected]) to get involved!
