Data update

Task/Evolutionary-algorithm/Elixir/evolutionary-algorithm.elixir

@@ -1,64 +0,0 @@
-defmodule Log do
-  def show(offspring,i) do
-    IO.puts "Generation: #{i}, Offspring: #{offspring}"
-  end
-
-  def found({target,i}) do
-    IO.puts "#{target} found in #{i} iterations"
-  end
-end
-
-defmodule Evolution do
-  # char list from A to Z; 32 is the ord value for space.
-  @chars  [32 | Enum.to_list(?A..?Z)]
-
-  def select(target) do
-    (1..String.length(target)) # Creates parent for generation 0.
-      |> Enum.map(fn _-> Enum.random(@chars) end)
-      |> mutate(to_charlist(target),0)
-      |> Log.found
-  end
-
-  # w is used to denote fitness in population genetics.
-
-  defp mutate(parent,target,i) when target == parent, do: {parent,i}
-  defp mutate(parent,target,i) do
-    w = fitness(parent,target)
-    prev = reproduce(target,parent,mu_rate(w))
-
-    # Check if the most fit member of the new gen has a greater fitness than the parent.
-    if w < fitness(prev,target) do
-      Log.show(prev,i)
-      mutate(prev,target,i+1)
-    else
-      mutate(parent,target,i+1)
-    end
-  end
-
-  # Generate 100 offspring and select the one with the greatest fitness.
-
-  defp reproduce(target,parent,rate) do
-    [parent | (for _ <- 1..100, do: mutation(parent,rate))]
-      |> Enum.max_by(fn n -> fitness(n,target) end)
-  end
-
-  # Calculate fitness by checking difference between parent and offspring chars.
-
-  defp fitness(t,r) do
-    Enum.zip(t,r)
-      |> Enum.reduce(0, fn {tn,rn},sum -> abs(tn - rn) + sum end)
-      |> calc
-  end
-
-  # Generate offspring based on parent.
-
-  defp mutation(p,r) do
-    # Copy the parent chars, then check each val against the random mutation rate
-    Enum.map(p, fn n -> if :rand.uniform <= r, do: Enum.random(@chars), else: n end)
-  end
-
-  defp calc(sum),  do: 100 * :math.exp(sum/-10)
-  defp mu_rate(n), do: 1   - :math.exp(-(100-n)/400)
-end
-
-Evolution.select("METHINKS IT IS LIKE A WEASEL")