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Genetic Algorithms in Elixir

Crossing Over More Than Two Parents

Explore how to implement crossover with more than two parents in genetic algorithms using Elixir. Understand key functions like Enum.chunk_every and Enum.reduce for combining parent chromosomes, and learn when multi-parent crossover adds complexity to your solutions.

We'll cover the following...

More than two parents crossover implementation

Some algorithms require us to select more than two parents for crossover. All of the algorithms presented in the previous lessons can be implemented on multiple parents.

Here’s an example of single-point crossover on multiple parents:

C++
def single_point_crossover([]), do:
  raise "You must have at least one parent!"
def single_point_crossover([p1 | []]), do: p1
def single_point_crossover(parents) do 
  crossover_point = :rand.uniform(hd(parents).size) 
  parents
  |> Enum.chunk_every(2, 1, [hd(parents)])
  |> Enum.map(&(List.to_tuple(&1)))
  |> Enum.reduce(
    [],
    fn {p1, p2}, chd ->
      {front, _} = Enum.split(p1.genes, crossover_point)
      {_, back} = Enum.split(p2.genes, crossover_point)
      c = %Chromosome{genes: front ++ back, size: length(p1)} 
      [c | chd]
    end
  )
end

Let us start by examining the first two function definitions. Thanks to Elixir’s rich set of pattern matching features, we can define ...