Running Your Solution

Genetic Algorithms in Elixir — by Sean Moriarity (16 / 101)

👈 Creating Children | TOC | Adding Mutation 👉

Your genetic algorithm will now look something like this:

​ population = for _ <- 1..100, ​do​: for _ <- 1..1000, ​do​: Enum.random(0..1)
​ 
​ evaluate =
​   ​fn​ population ->
​     Enum.sort_by(population, &Enum.sum/1, &>=/2)
​   ​end​
​ 
​ selection =
​   ​fn​ population ->
​     population
​     |> Enum.chunk_every(2)
​     |> Enum.map(&List.to_tuple(&1))
​   ​end​
​ 
​ crossover =
​   ​fn​ population ->
​     Enum.reduce(population, [],
​       ​fn​ {p1, p2}, acc ->
​         cx_point = ​:rand​.uniform(1000)
​         {{h1, t1}, {h2, t2}} =
​           {Enum.split(p1, cx_point),
​           Enum.split(p2, cx_point)}
​         [h1 ++ t2, h2 ++ t1 | acc]
​       ​end​
​     )
​   ​end​
​ 
​ algorithm =
​   ​fn​ population, algorithm ->
​     best = Enum.max_by(population, &Enum.sum/1)
​     IO.write(​"​​\rCurrent Best: "​ <> Integer.to_string(Enum.sum(best)))
​     ​if​ Enum.sum(best) == 1000 ​do​
​       best
​     ​else​
​       population
​       |> evaluate.()
​       |> selection.()
​       |> crossover.()
​       |> algorithm.(algorithm)
​     ​end​
​   ​end​

Your algorithm now has all of the necessary components it needs to produce a solution; however, you might be wondering why the mutation step was left out. You’ll find out in a minute — before then, try running your algorithm to ensure that everything works…