NoveltyModifier

abstract def combine(a1: A, a2: A): A

abstract def diff(original: A, modified: A): A

abstract def diversity(values: Seq[Seq[Double]]): Seq[Lazy[Double]]

Compute the diversity metric of the values

abstract def fitness: SimpleLens[F, Seq[Double]]

abstract def individualDistance(g: Seq[Individual[G, P, F]]): Seq[Lazy[Double]]

abstract def initialArchive: A

abstract def isDominated(p1: Seq[Double], p2: Seq[Double]): Boolean

abstract def toArchive(individuals: Seq[Individual[G, P, F]]): A

final def !=(arg0: Any): Boolean

final def ##(): Int

final def ==(arg0: Any): Boolean

def archive(a: A, individuals: Seq[Individual[G, P, F]]): A

final def asInstanceOf[T0]: T0

def clone(): AnyRef

def diversity: SimpleLens[RankDiversity, Lazy[Double]]

final def eq(arg0: AnyRef): Boolean

def equals(arg0: Any): Boolean

def filter(individuals: Seq[Individual[G, P, F]]): Seq[Individual[G, P, F]]

Filter the individuals

def finalize(): Unit

def fitnesses(evaluated: Seq[Individual[G, P, F]], archive: A): Seq[Seq[Double]]

final def getClass(): Class[_]

def hashCode(): Int

final def isInstanceOf[T0]: Boolean

def modify(evaluated: Seq[Individual[G, P, F]], archive: A): Population[G, P, F, MF]

Generate a population from a set of indiviuals

final def ne(arg0: AnyRef): Boolean

def neighbours[A](distance: (A, A) ⇒ Double)(a: A, all: Seq[A], nb: Int): Seq[A]

final def notify(): Unit

final def notifyAll(): Unit

def rank(values: Seq[Seq[Double]]): Seq[Lazy[Int]]

Compute the rank of a set of individuals.

def rank: SimpleLens[RankDiversity, Lazy[Int]]

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

def toPopulation(individuals: Seq[Individual[G, P, F]], a: A): Population[G, P, F, MF]

Generate a population from a set of indiviuals that is filtered in a first time

def toString(): String

final def wait(): Unit

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

final def wait(arg0: Long): Unit