mgo/mgo.evolution.algorithm/NoisyProfile

NoisyProfile

object NoisyProfile
Companion
class
trait Product
trait Mirror
class Object
trait Matchable
class Any

Type members

Classlikes

case
class Result[N, P](continuous: Vector[Double], discrete: Vector[Int], fitness: Vector[Double], niche: N, replications: Int, individual: Individual[P])

Types

Inherited types

type MirroredElemLabels <: Tuple

The names of the product elements

The names of the product elements

Inherited from
Mirror
type MirroredLabel <: String

The name of the type

The name of the type

Inherited from
Mirror

Value members

Concrete methods

def adaptiveBreeding[P : Manifest](lambda: Int, operatorExploration: Double, cloneProbability: Double, aggregation: Vector[P] => Vector[Double], discrete: Vector[D], reject: Option[Genome => Boolean]): (ProfileState, Individual[P]) => Genome
def aggregatedFitness[N, P : Manifest](aggregation: Vector[P] => Vector[Double]): Individual[P] => Vector[Double]
def boundedContinuousProfile[P](continuous: Vector[C], x: Int, nX: Int, min: Double, max: Double): Individual[P] => Int
def boundedObjectiveProfile[P : Manifest](aggregation: Vector[P] => Vector[Double], x: Int, nX: Int, min: Double, max: Double): Individual[P] => Int
def continuousProfile[P](x: Int, nX: Int): Individual[P] => Int
def discreteProfile[P](x: Int): Individual[P] => Int
def elitism[N, P : Manifest](niche: Individual[P] => N, muByNiche: Int, historySize: Int, aggregation: Vector[P] => Vector[Double], components: Vector[C]): ProfileState => Individual[P]
def expression[P : Manifest](fitness: (Random, Vector[Double], Vector[Int]) => P, continuous: Vector[C]): (Random, Genome) => Individual[P]
def gridContinuousProfile[P](continuous: Vector[C], x: Int, intervals: Vector[Double]): Individual[P] => Int
def gridObjectiveProfile[P : Manifest](aggregation: Vector[P] => Vector[Double], x: Int, intervals: Vector[Double]): Individual[P] => Int
def initialGenomes(lambda: Int, continuous: Vector[C], discrete: Vector[D], reject: Option[Genome => Boolean], rng: Random): Vector[Genome]
def reject[N, P](pse: NoisyProfile[N, P]): Option[Genome => Boolean]
def result[N, P : Manifest](population: Vector[Individual[P]], aggregation: Vector[P] => Vector[Double], niche: Individual[P] => N, continuous: Vector[C], onlyOldest: Boolean, keepAll: Boolean): Vector[Result[N, P]]
def result[N, P : Manifest](noisyProfile: NoisyProfile[N, P], population: Vector[Individual[P]], onlyOldest: Boolean): Vector[Result[N, P]]

Implicits

Implicits

implicit
def isAlgorithm[N, P : Manifest]: Algorithm[NoisyProfile[N, P], Individual[P], Genome, ProfileState]