Rank-based selection. Sorts the population by fitness and assigns selection probability by rank (not raw fitness value).

Resistant to outliers — a chromosome with 100× the fitness of the rest won't dominate the selection pool.

Fitness-proportional (roulette wheel) selection.

Each individual's selection probability is proportional to its fitness. Requires all fitness values to be non-negative and total fitness > 0.

Dispatches to the named selection operator.

Valid operators: :tournament, :roulette, :rank, :sus.

The config map must contain :population_size. Tournament selection also reads :tournament_size (defaults to 3).

Example

iex> pop = [
...>   {%Petri.Chromosome.Binary{genes: [0, 0, 1]}, 1.0},
...>   {%Petri.Chromosome.Binary{genes: [1, 1, 0]}, 2.0},
...>   {%Petri.Chromosome.Binary{genes: [1, 1, 1]}, 3.0}
...> ]
iex> parents = Petri.Selection.select(:tournament, pop, %{population_size: 2, tournament_size: 2})
iex> length(parents)
2

Stochastic Universal Sampling. Places population_size equally-spaced pointers on the roulette wheel.

Guarantees that very fit individuals are selected at least floor(p_i * n) times, giving lower variance than repeated roulette spins.

Tournament selection. Picks tournament_size random individuals and returns the fittest. Repeat population_size times.

Fast, works with negative fitness, and doesn't require fitness scaling.