TODO

1. Provide a Storable instance for AI.MEP.Types.Gene
   and make the Chromosome a Data.Vector.Storable.

2. Improve code generation:
     Perform subexpression elimination,
     e.g. x0 / x0 should be reduced to 1
          1 + 1 should become 2, etc.

     And possibly, support unary operators:
          Interpreted expression

            v1 = x0 / x0
            v3 = s x0 v1
            v6 = v3 / v1
            v14 = s x0 v6
            v15 = v14 * v6
            result = v1 - v15

          should eventually become

            v1 = sin x0
            v2 = v1 * v1
            result = 1 - v2

3! Redefine functions `F` to have arbitrary arity instead of 2:
   (e.g. `type F a = (BS, Vector a -> a)`).
   Use a bytestring BS instead of Char to represent a function
   OR represent functions with an abstract type + evaluate table
   (potentially slower).

4. Performance tuning and benchmarking using Criterion package.
   Hint: use of matrices featuring O(1) memory access
   instead of lists of vectors ([Chromosome a], [Phenotype a]),
   might improve the speed of such operators as binaryTournament.

5! Implement subpopulations that eventually exchange individuals

6. The following declaration of `newGene` (type class `Genetic`)
   might be preferable

     newGene :: (PrimMonad m) => Int -> RandT m (Gene a Int)