mgo.evolution.algorithm.NoisyNSGA2
See theNoisyNSGA2 companion class
object NoisyNSGA2
Attributes
- Companion
- class
- Graph
-
- Supertypes
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trait Producttrait Mirrorclass Objecttrait Matchableclass Any
- Self type
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NoisyNSGA2.type
Members list
Type members
Classlikes
case class Result[P](continuous: Vector[Double], discrete: Vector[Int], fitness: Vector[Double], replications: Int, individual: Individual[P])
Attributes
- Supertypes
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trait Serializabletrait Producttrait Equalsclass Objecttrait Matchableclass AnyShow all
Types
Inherited types
The names of the product elements
The names of the product elements
Attributes
- Inherited from:
- Mirror
The name of the type
The name of the type
Attributes
- Inherited from:
- Mirror
Value members
Concrete methods
def adaptiveBreeding[S, P : Manifest](lambda: Int, operatorExploration: Double, cloneProbability: Double, aggregation: (Vector[P]) => Vector[Double], discrete: Vector[D], reject: Option[Genome => Boolean]): (S, Individual[P]) => Genome
def elitism[S, P : Manifest](mu: Int, historySize: Int, aggregation: (Vector[P]) => Vector[Double], components: Vector[C]): S => Individual[P]
def expression[P : Manifest](phenotype: (Random, Vector[Double], Vector[Int]) => P, continuous: Vector[C]): (Random, Genome, Long, Boolean) => Individual[P]
def fitness[P : Manifest](aggregation: (Vector[P]) => Vector[Double]): (Individual[P]) => Vector[Double]
def initialGenomes(lambda: Int, continuous: Vector[C], discrete: Vector[D], reject: Option[Genome => Boolean], rng: Random): Vector[Genome]
def result[P : Manifest](population: Vector[Individual[P]], aggregation: (Vector[P]) => Vector[Double], continuous: Vector[C], keepAll: Boolean): Vector[Result[P]]
def result[P : Manifest](nsga2: NoisyNSGA2[P], population: Vector[Individual[P]]): Vector[Result[P]]
Implicits
Implicits
In this article