mgo
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mgo/mgo/mgo.evolution/mgo.evolution.algorithm/NoisyProfile

NoisyProfile

mgo.evolution.algorithm.NoisyProfile
See theNoisyProfile companion class
object NoisyProfile

Attributes

Companion
class
Graph
Supertypes
trait Product
trait Mirror
class Object
trait Matchable
class Any
Self type

Members list

Type members

Classlikes

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

Attributes

Supertypes
trait Serializable
trait Product
trait Equals
class Object
trait Matchable
class Any
Show all

Types

Inherited types

type MirroredElemLabels <: Tuple

The names of the product elements

The names of the product elements

Attributes

Inherited from:
Mirror
type MirroredLabel <: String

The name of the type

The name of the type

Attributes

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, Long, Boolean) => 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]
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