Solving One-Max for the Last Time

Genetic Algorithms in Elixir — by Sean Moriarity (31 / 101)

👈 Understanding and Choosing Genotypes | TOC | Spelling Words with Genetic Algorithms 👉

To get your feet wet with the Problem behaviour you created in this chapter, start small by solving the One-Max problem. You already know what the three problem-specific parameters should look like; all you have to do is fit them into the framework you defined in this chapter.

Open the scripts/one_max.exs file and replace the contents with this:

​ ​defmodule​ OneMax ​do​
​   @behaviour Problem
​   alias Types.Chromosome
​ 
​   @impl true
​   ​def​ genotype ​do​
​     genes = for _ <- 1..42, ​do​: Enum.random(0..1)
​     %Chromosome{​genes:​ genes, ​size:​ 42}
​   ​end​
​ 
​   @impl true
​   ​def​ fitness_function(chromosome), ​do​: Enum.sum(chromosome.genes)
​ 
​   @impl true
​   ​def​ terminate?([best | _]), ​do​: best.fitness == 42
​ ​end​

By now, you should be familiar with these functions, as they are identical to the functions you implemented in your previous attempts at solving the One-Max problem. This time, however, they fit within your problem behaviour.

To run your solution, add the following code below your module definition:

​ soln = Genetic.run(OneMax)
​ 
​ IO.write(​"​​\n"​)
​ IO.inspect(soln)

Now, run one_max.exs:

​ ​$ ​​mix​​ ​​run​​ ​​scripts/one_max.exs​
​ Current Best: 42
​ %Types.Chromosome{
​   age: 1,
​   fitness: 42,
​   genes: [1, 1,..1],
​   size: 42
​ }

👈 Understanding and Choosing Genotypes | TOC | Spelling Words with Genetic Algorithms 👉

Genetic Algorithms in Elixir by Sean Moriarity can be purchased in other book formats directly from the Pragmatic Programmers. If you notice a code error or formatting mistake, please let us know here so that we can fix it.