ModelFamilyElitism

abstract def aggregate(fitness: F): Double

abstract def archive(a: A, oldIndividuals: Population[G, P, F], offspring: Population[G, P, F])(implicit rng: Random): A

abstract def clamp(values: SimpleLens[G, Seq[Double]]): SimpleLens[G, Seq[Double]]

abstract def genomeSize: Int

Size of the value part of the genome

abstract def initialArchive(implicit rng: Random): A

abstract def models: Int

abstract def nicheSize: Int

final def !=(arg0: Any): Boolean

final def ##(): Int

final def ==(arg0: Any): Boolean

final def asInstanceOf[T0]: T0

def clone(): AnyRef

def computeElitism(oldGeneration: Population[G, P, F], offspring: Population[G, P, F], archive: A)(implicit rng: Random): Population[G, P, F]

def elitism(oldGeneration: Population[G, P, F], offspring: Population[G, P, F], archive: A)(implicit rng: Random): Population[G, P, F]

final def eq(arg0: AnyRef): Boolean

def equals(arg0: Any): Boolean

def filter(population: Population[G, P, F]): Population[G, P, F]

Filter the individuals

def finalize(): Unit

def genomesEqualOn(g: G): Any

final def getClass(): Class[_]

def hashCode(): Int

final def isInstanceOf[T0]: Boolean

def keep(individuals: Population[G, P, F])(implicit rng: Random): Population[G, P, F]

def modelId: Lens[G, G, Int, Int]

final def ne(arg0: AnyRef): Boolean

def niche(individual: Individual[G, P, F]): Int

final def notify(): Unit

final def notifyAll(): Unit

def randomGenome(implicit rng: Random): Genome

def rawValues: Lens[G, G, Seq[Double], Seq[Double]]

def sigma: Lens[G, G, Seq[Double], Seq[Double]]

final def synchronized[T0](arg0: ⇒ T0): T0

def toString(): String

def values: SimpleLens[G, Seq[Double]]

The value part of the genome actually used for the optimisation

final def wait(): Unit

final def wait(arg0: Long, arg1: Int): Unit

final def wait(arg0: Long): Unit