Genetic Algorithms in Elixir!

Genex is a simple library for creating Genetic Algorithms in Elixir. A Genetic Algorithm is a search-based optimization technique based on the principles of Genetics and Natural Selection.

The basic life-cycle of a Genetic Algorithm is as follows:

1) Initialize the Population 2) Loop until goal is reached a) Select Parents b) Perform Crossover c) Mutate some of population d) Select Survivors

Genex uses a structure similar to the one above, using callbacks to give you full control over your genetic algorithm.

Implementation

Genex requires an implementation module:

defmodule OneMax do
  use Genex

  def encoding do
    for _ <- 1..15, do: Enum.random(0..1)
  end

  def fitness_function(chromosome), do: Enum.sum(chromosome.genes)

  def terminate?(population), do: population.max_fitness == 15
end

Genex requires 3 function definitions: encoding/0, fitness_function/1, and terminate?/1. Let's take a closer look at each of these:

Encoding

def encoding do
  for _ <- 1..15, do: Enum.random(0..1)
end

encoding/0 defines your encoding of the chromosome's genes for your use-case. In the example above, we define a Binary Gene Set of length 15. Genex uses this function to generate an initial population of Chromosomes matching your encoding.

Fitness Function

def fitness_function(chromosome) do
  Enum.sum(chromosome.genes)
end

fitness_function/1 defines how the algorithm evaluates the fitness of a chromosome. It takes in a chromosome struct and returns a number. Fitness is how your algorithm determines which chromosomes should be persisted to the next generation as well as which chromosomes should be selected to crossover. In this case, we want to maximize 1's in our set of genes, so we define fitness as the sum of genes.

Termination Criteria

def terminate?(population) do
  population.max_fitness == 15
end

terminate?/1 defines the termination criteria for your algorithm. This tells Genex when your algorithm should stop running. In this case we use a Max Fitness; however, you can also tell the algorithm to stop after a certain number of generations.

Running

Once you have defined an implementation module. Utilize the run/0 function to run the algorithm. The function will return the solution population for analysis. You can display a summary of the solution with the: Genex.Visualizers.Text.display_summary/1 function.

soln = OneMax.run()
Genex.Visualizers.Text.display_summary(soln)

Configuration

Genex offers a number of settings to adjust the algorithm to your liking. You can adjust: strategies, rates, and more. See the configuration guide for a full list.

Customization

You can customize every step of your genetic algorithm utilizing one or more of the many callbacks Genex provides. See the customization guide for more.