mgo/mgo.evolution/ranking

ranking

object ranking
class Object
trait Matchable
class Any

Type members

Classlikes

object Ranking

Types

type Ranking[M[_], I] = Kleisli[M, Vector[I], Vector[Later[Int]]]

Compute the ranks of the individuals in the same order

Compute the ranks of the individuals in the same order

Value members

Concrete methods

def hitCountRanking[S, I](s: S, population: Vector[I], cell: I => Vector[Int], hitmap: Lens[S, HitMap]): Vector[Int]
def monoObjectiveRanking[M[_] : Monad, I](fitness: I => Double): Ranking[M, I]
def numberOfDominating[I](fitness: I => Vector[Double], values: Vector[I], dominance: Dominance): Vector[Later[Int]]
def paretoRanking[I](population: Vector[I], fitness: I => Vector[Double], dominance: Dominance): Vector[Eval[Int]]

** Generic functions on rankings ***

** Generic functions on rankings ***

def paretoRankingMinAndCrowdingDiversity[I](population: Vector[I], fitness: I => Vector[Double], random: Random): Vector[(Eval[Int], Double)]
def rank[M[_] : Monad, I, K](ranking: Kleisli[M, Vector[I], Vector[K]]): Kleisli[M, Vector[I], Vector[(I, K)]]
def rankAndDiversity[M[_] : Monad, I](ranking: Ranking[M, I], diversity: Diversity[M, I]): Kleisli[M, Vector[I], Vector[(Later[Int], Later[Double])]]
def worstParetoRanking: (Later[Int], Double)