Scaling

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
abstract type F

Definition Classes
F
abstract type G

Definition Classes
G
abstract type P

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

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

Definition Classes
Problem
abstract def express(g: G, rng: Random): P

Definition Classes
Problem
abstract def genomeSize: Int

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

Definition Classes
GA
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 max: Seq[Double]

Maximum scaled value in the correct order
abstract def min: Seq[Double]

minimum scaled value in the correct order
abstract def randomGenome(implicit rng: Random): G

Definition Classes
GA → RandomGenome
abstract def rawValues: SimpleLens[G, Seq[Double]]

Definition Classes
GA
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
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 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 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 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
final def ne(arg0: AnyRef): Boolean

Definition Classes
AnyRef
final def notify(): Unit

Definition Classes
AnyRef
final def notifyAll(): Unit

Definition Classes
AnyRef
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 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 Scaling extends Problem with GA with GAScaling

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

abstract type F

abstract type G

abstract type P

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

abstract def express(g: G, rng: Random): P

abstract def genomeSize: Int

abstract def initialArchive(implicit rng: Random): A

abstract def initialState: STATE

abstract def lambda: Int

abstract def max: Seq[Double]

abstract def min: Seq[Double]

abstract def randomGenome(implicit rng: Random): G

abstract def rawValues: SimpleLens[G, Seq[Double]]

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

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

def finalize(): Unit

def genomesEqualOn(g: G): Any

final def getClass(): Class[_]

def hashCode(): Int

final def isInstanceOf[T0]: Boolean

final def ne(arg0: AnyRef): Boolean

final def notify(): Unit

final def notifyAll(): Unit

def scale(x: 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 GAScaling

Inherited from genome.GA

Inherited from GenomeClamping

Inherited from RandomGenome

Inherited from Problem

Inherited from Evolution

Inherited from elitism.Elitism

Inherited from elitism.IndividualFilter

Inherited from breed.Breeding

Inherited from archive.Archive

Inherited from archive.A

Inherited from Lambda

Inherited from termination.Termination

Inherited from fitness.F

Inherited from phenotype.P

Inherited from genome.G

Inherited from AnyRef

Inherited from Any

Ungrouped