RastriginVector

Type Members

abstract type A

Definition Classes
Archive → A
case class EvolutionState(population: Population[Evolution.G, Evolution.P, Evolution.F], archive: Evolution.A, generation: Int, terminationState: Evolution.STATE, terminated: Boolean) extends Product with Serializable

Represent a state of the evolution algorithm
Represent a state of the evolution algorithm

Definition Classes
Evolution
type F = Seq[Double]

Definition Classes
MGFitness → F
type G = Genome

Definition Classes
ModelFamilyGenome → G
type P = Double

Definition Classes
RastriginVector → P
abstract type STATE

Type of the state maintained to study the evolution of the algorithm
Type of the state maintained to study the evolution of the algorithm

Definition Classes
Termination

Abstract Value Members

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

Definition Classes
Archive
abstract def breed(population: Population[G, P, F], a: A, size: Int)(implicit rng: Random): Seq[G]

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

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

Definition Classes
Elitism
abstract def initialArchive(implicit rng: Random): A

Definition Classes
Archive
abstract def initialState: STATE

Compute the initial state
Compute the initial state
returns
the initial state

Definition Classes
Termination
abstract def lambda: Int

the size of the offspring
the size of the offspring

Definition Classes
Lambda
abstract def modelMasks: Range
abstract def terminated(population: Population[G, P, F], terminationState: STATE)(implicit rng: Random): (Boolean, STATE)

Test if the algorithm has converged.
Test if the algorithm has converged.
population
the current population
terminationState
the actual termination state
returns
a boolean which is equal to true if a terminal state has been detected and the new termination state

Definition Classes
Termination

Concrete Value Members

final def !=(arg0: Any): Boolean

Definition Classes
AnyRef → Any
final def ##(): Int

Definition Classes
AnyRef → Any
final def ==(arg0: Any): Boolean

Definition Classes
AnyRef → Any
final def asInstanceOf[T0]: T0

Definition Classes
Any
lazy val bestFitness: IndexedSeq[Int]
def buildRNG(seed: Long): Random

Definition Classes
Evolution
def clone(): AnyRef

Attributes
protected[java.lang]
Definition Classes
AnyRef
Annotations
@throws( ... )
def elitism(oldGeneration: Population[G, P, F], offspring: Population[G, P, F], archive: A)(implicit rng: Random): Population[G, P, F]

Definition Classes
Elitism
final def eq(arg0: AnyRef): Boolean

Definition Classes
AnyRef
def equals(arg0: Any): Boolean

Definition Classes
AnyRef → Any
def evaluate(phenotype: P, rng: Random): F

Evaluate a phenotype
Evaluate a phenotype
phenotype
the phenotype to evaluate
returns
the phenotype

Definition Classes
RastriginVector → Problem
def evolve(implicit rng: Random): Iterator[EvolutionState]

Definition Classes
Problem
def evolve(expression: (G, Random) ⇒ P, evaluation: (P, Random) ⇒ F)(implicit prng: Random): Iterator[EvolutionState]

Run the evolutionary algorithm
Run the evolutionary algorithm
expression
the genome expression
evaluation
the fitness evaluator
returns
an iterator over the states of the evolution

Definition Classes
Evolution
def evolve(population: Population[G, P, F], a: A, expression: (G, Random) ⇒ P, evaluation: (P, Random) ⇒ F)(implicit rng: Random): Iterator[EvolutionState]

Run the evolutionary algorithm
Run the evolutionary algorithm
population
the initial individuals
expression
the genome expression
evaluation
the fitness evaluator
returns
an iterator over the states of the evolution

Definition Classes
Evolution
def express(g: G, rng: Random): Double

Definition Classes
RastriginVector → Problem
def filter(population: Population[G, P, F]): Population[G, P, F]

Filter the individuals
Filter the individuals
population
the set of evaluated individuals
returns
the filtrated individuals

Definition Classes
IndividualFilter
def finalize(): Unit

Attributes
protected[java.lang]
Definition Classes
AnyRef
Annotations
@throws( classOf[java.lang.Throwable] )
def fitness(f: F): F

Definition Classes
MGFitness → MG
def fitness(individual: Individual[G, P, F]): Seq[Double]

Definition Classes
MG
lazy val genomeSize: Int

Size of the value part of the genome
Size of the value part of the genome

Definition Classes
RastriginVector → ModelFamilyGenome → GA
def genomesEqualOn(g: G): Any

Definition Classes
GA → G
final def getClass(): Class[_]

Definition Classes
AnyRef → Any
def hashCode(): Int

Definition Classes
AnyRef → Any
final def isInstanceOf[T0]: Boolean

Definition Classes
Any
lazy val masks: Seq[Seq[Boolean]]
lazy val max: Seq[Double]

Maximum scaled value in the correct order
Maximum scaled value in the correct order

Definition Classes
RastriginVector → Scaling
lazy val min: Seq[Double]

minimum scaled value in the correct order
minimum scaled value in the correct order

Definition Classes
RastriginVector → Scaling
def modelId: Lens[G, G, Int, Int]

Definition Classes
ModelFamilyGenome → ModelId
def models: Int

Definition Classes
RastriginVector → ModelFamilyGenome
lazy val nbTrue: Seq[Int]
final def ne(arg0: AnyRef): Boolean

Definition Classes
AnyRef
final def notify(): Unit

Definition Classes
AnyRef
final def notifyAll(): Unit

Definition Classes
AnyRef
def randomGenome(implicit rng: Random): Genome

Definition Classes
ModelFamilyGenome → GA → RandomGenome
def rawValues: Lens[G, G, Seq[Double], Seq[Double]]

Definition Classes
ModelFamilyGenome → GA
def scale(x: Seq[Double]): Seq[Double]

Scale a vector according to the minimun and maximum
Scale a vector according to the minimun and maximum
x
the vector to scale
returns
the scaled vector

Definition Classes
Scaling → GAScaling
def sigma: Lens[G, G, Seq[Double], Seq[Double]]

Definition Classes
ModelFamilyGenome → Sigma
def step(population: Population[G, P, F], archive: A, expression: (G, Random) ⇒ P, evaluation: (P, Random) ⇒ F)(implicit rng: Random): (Population[G, P, F], A)

Evolve one step
Evolve one step
population
the current population
archive
the current archive
expression
expression of the genome
evaluation
the fitness evaluator
returns
a new population of evaluated solutions

Definition Classes
Evolution
final def synchronized[T0](arg0: ⇒ T0): T0

Definition Classes
AnyRef
def toString(): String

Definition Classes
AnyRef → Any
def values: SimpleLens[G, Seq[Double]]

The value part of the genome actually used for the optimisation
The value part of the genome actually used for the optimisation

Definition Classes
GA
final def wait(): Unit

Definition Classes
AnyRef
Annotations
@throws( ... )
final def wait(arg0: Long, arg1: Int): Unit

Definition Classes
AnyRef
Annotations
@throws( ... )
final def wait(arg0: Long): Unit

Definition Classes
AnyRef
Annotations
@throws( ... )

trait RastriginVector extends problem.Problem with problem.Scaling with ModelFamilyGenome with fitness.MGFitness

Type Members

abstract type A

case class EvolutionState(population: Population[Evolution.G, Evolution.P, Evolution.F], archive: Evolution.A, generation: Int, terminationState: Evolution.STATE, terminated: Boolean) extends Product with Serializable

type F = Seq[Double]

type G = Genome

type P = Double

abstract type STATE

Abstract Value Members

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

abstract def breed(population: Population[G, P, F], a: A, size: Int)(implicit rng: Random): Seq[G]

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

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

abstract def initialArchive(implicit rng: Random): A

abstract def initialState: STATE

abstract def lambda: Int

abstract def modelMasks: Range

abstract def terminated(population: Population[G, P, F], terminationState: STATE)(implicit rng: Random): (Boolean, STATE)

Concrete Value Members

final def !=(arg0: Any): Boolean

final def ##(): Int

final def ==(arg0: Any): Boolean

final def asInstanceOf[T0]: T0

lazy val bestFitness: IndexedSeq[Int]

def buildRNG(seed: Long): Random

def clone(): AnyRef

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 evaluate(phenotype: P, rng: Random): F

def evolve(implicit rng: Random): Iterator[EvolutionState]

def evolve(expression: (G, Random) ⇒ P, evaluation: (P, Random) ⇒ F)(implicit prng: Random): Iterator[EvolutionState]

def evolve(population: Population[G, P, F], a: A, expression: (G, Random) ⇒ P, evaluation: (P, Random) ⇒ F)(implicit rng: Random): Iterator[EvolutionState]

def express(g: G, rng: Random): Double

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

def finalize(): Unit

def fitness(f: F): F

def fitness(individual: Individual[G, P, F]): Seq[Double]

lazy val genomeSize: Int

def genomesEqualOn(g: G): Any

final def getClass(): Class[_]

def hashCode(): Int

final def isInstanceOf[T0]: Boolean

lazy val masks: Seq[Seq[Boolean]]

lazy val max: Seq[Double]

lazy val min: Seq[Double]

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

def models: Int

lazy val nbTrue: Seq[Int]

final def ne(arg0: AnyRef): Boolean

final def notify(): Unit

final def notifyAll(): Unit

def randomGenome(implicit rng: Random): Genome

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

def scale(x: Seq[Double]): Seq[Double]

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

def step(population: Population[G, P, F], archive: A, expression: (G, Random) ⇒ P, evaluation: (P, Random) ⇒ F)(implicit rng: Random): (Population[G, P, F], A)

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

def toString(): String

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

final def wait(): Unit

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

final def wait(arg0: Long): Unit

Inherited from fitness.MGFitness

Inherited from fitness.MG

Inherited from ModelFamilyGenome

Inherited from genome.Sigma

Inherited from ModelId

Inherited from problem.Scaling

Inherited from GAScaling

Inherited from genome.GA

Inherited from GenomeClamping

Inherited from RandomGenome

Inherited from problem.Problem

Inherited from Evolution

Inherited from elitism.Elitism

Inherited from elitism.IndividualFilter

Inherited from breed.Breeding

Inherited from archive.Archive