CMAES

Type Members

type A = archive.CMAESArchive.Archive

Definition Classes
CMAESArchive → 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
type G = Genome

Definition Classes
GAGenomeWithRandomValue → 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 fitness(f: F): Seq[Double]

Definition Classes
MG
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 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 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
def activeCMAES: Boolean

Definition Classes
CMAESArchive
def aggregate(f: F): Double

Definition Classes
MaxAggregation → Aggregation
def archive(a: A, oldIndividuals: Population[G, P, F], offspring: Population[G, P, F])(implicit rng: Random): A

Definition Classes
CMAESArchive → Archive
final def asInstanceOf[T0]: T0

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

Definition Classes
CMAESBreeding → Breeding
def buildRNG(seed: Long): Random

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

Definition Classes
ClampedGenome → GenomeClamping
def clone(): AnyRef

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

Definition Classes
KeepOffspringElitism → Elitism
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(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 fitness(individual: Individual[G, P, F]): Seq[Double]

Definition Classes
MG
def genomesEqualOn(g: G): Any

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

Definition Classes
AnyRef → Any
def guess(implicit rng: Random): Seq[Double]

Definition Classes
CMAESArchive
def hashCode(): Int

Definition Classes
AnyRef → Any
def initialArchive(implicit rng: Random): A

Definition Classes
CMAESArchive → Archive
def initialSigma: Double

Definition Classes
CMAESArchive
final def isInstanceOf[T0]: Boolean

Definition Classes
Any
def minimumSigma: Double

Definition Classes
MinimumSigma
def mu(lambda: Int): Int

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

Definition Classes
AnyRef
def negalphaold: Double

Definition Classes
CMAESArchive
def negminresidualvariance: Double

Definition Classes
CMAESArchive
final def notify(): Unit

Definition Classes
AnyRef
final def notifyAll(): Unit

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

Definition Classes
GAGenomeWithRandomValue → GA → RandomGenome
def randomValues: SimpleLens[G, Seq[Double]]

Definition Classes
GAGenomeWithRandomValue → RandomValue
def rawValues: SimpleLens[G, Seq[Double]]

Definition Classes
GAGenomeWithRandomValue → GA
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 CMAES extends Evolution with KeepOffspringElitism with GAGenomeWithRandomValue with MaxAggregation with CMAESBreeding with CMAESArchive with ClampedGenome

Type Members

type A = archive.CMAESArchive.Archive

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

type G = Genome

abstract type P

abstract type STATE

Abstract Value Members

abstract def fitness(f: F): Seq[Double]

abstract def genomeSize: Int

abstract def initialState: STATE

abstract def lambda: Int

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

def activeCMAES: Boolean

def aggregate(f: F): Double

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

final def asInstanceOf[T0]: T0

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

def buildRNG(seed: Long): Random

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

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

def genomesEqualOn(g: G): Any

final def getClass(): Class[_]

def guess(implicit rng: Random): Seq[Double]

def hashCode(): Int

def initialArchive(implicit rng: Random): A

def initialSigma: Double

final def isInstanceOf[T0]: Boolean

def minimumSigma: Double

def mu(lambda: Int): Int

final def ne(arg0: AnyRef): Boolean

def negalphaold: Double

def negminresidualvariance: Double

final def notify(): Unit

final def notifyAll(): Unit

def randomGenome(implicit rng: Random): Genome

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

def rawValues: SimpleLens[G, 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 genome.ClampedGenome

Inherited from breed.CMAESBreeding

Inherited from archive.CMAESArchive

Inherited from MinimumSigma

Inherited from fitness.MaxAggregation

Inherited from fitness.MG

Inherited from fitness.Aggregation

Inherited from genome.GAGenomeWithRandomValue

Inherited from genome.RandomValue

Inherited from genome.GA

Inherited from GenomeClamping

Inherited from RandomGenome

Inherited from elitism.KeepOffspringElitism

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