Himmelblau

Type Members

abstract type A

Definition Classes
Archive → A
case class EvolutionState(population: Population[Evolution.G, Evolution.P, Evolution.F, Evolution.MF], 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
abstract type G

Definition Classes
G
abstract type MF

the type of the meta-fitness
the type of the meta-fitness

Definition Classes
MF
type P = Seq[Double]

Definition Classes
Himmelblau → 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 breed(individuals: Seq[Individual[G, P, F]], a: A, size: Int)(implicit aprng: Random): Seq[G]

Definition Classes
Breeding
abstract def combine(a1: A, a2: A): A

Definition Classes
Archive
abstract def diff(original: A, modified: A): A

Definition Classes
Archive
abstract def elitism(oldGeneration: Seq[Individual[G, P, F]], newGeneration: Seq[Individual[G, P, F]], archive: A)(implicit aprng: Random): Seq[Individual[G, P, F]]

Definition Classes
Elitism
abstract def genome: SimpleLens[G, Seq[Double]]

The entire genome with potentially additional information
The entire genome with potentially additional information

Definition Classes
GA
abstract def initialArchive: 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 modify(individuals: Seq[Individual[G, P, F]], archive: A): Population[G, P, F, MF]

Generate a population from a set of indiviuals
Generate a population from a set of indiviuals
individuals
a set of individual
returns
the population with the meta-fitness for each individual

Definition Classes
Modifier
abstract def randomGenome(implicit rng: Random): G

Definition Classes
GA → RandomGenome
abstract def terminated(population: ⇒ Population[G, P, F, MF], terminationState: STATE): (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
abstract def toArchive(individuals: Seq[Individual[G, P, F]]): A

Definition Classes
Archive
abstract 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

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

Definition Classes
Archive
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( ... )
final def eq(arg0: AnyRef): Boolean

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

Definition Classes
AnyRef → Any
def evaluate(x: Seq[Double], rng: Random): Seq[Double]

Evaluate a phenotype
Evaluate a phenotype
returns
the phenotype

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

Definition Classes
Scaling → 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(individuals: Seq[Individual[G, P, F]], a: A, expression: (G, Random) ⇒ P, evaluation: (P, Random) ⇒ F)(implicit aprng: Random): Iterator[EvolutionState]

Run the evolutionary algorithm
Run the evolutionary algorithm
individuals
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): List[Double]

Definition Classes
Himmelblau → Problem
def filter(individuals: Seq[Individual[G, P, F]]): Seq[Individual[G, P, F]]

Filter the individuals
Filter the individuals
individuals
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: SimpleLens[Seq[Double], F]

Definition Classes
MGFitness → MG
def genomeSize: Int

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

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

Definition Classes
AnyRef → Any
def hashCode(): Int

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

Definition Classes
Any
def max: List[Double]

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

Definition Classes
Himmelblau → Scaling
def min: List[Double]

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

Definition Classes
Himmelblau → Scaling
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(i: Individual[G, P, F]): Individual[G, P, F]

Definition Classes
Scaling
def scale[MF](i: PopulationElement[G, P, F, MF]): PopulationElement[G, P, F, MF]

Scale a population element genome from [0.0, 1.0] to the correct scale
Scale a population element genome from [0.0, 1.0] to the correct scale
i
the population element to scale
returns
the scaled population element

Definition Classes
Scaling
def scale(g: G): G

Scale the genome from [0.0, 1.0] to the correct scale for the fitness evaluation
Scale the genome from [0.0, 1.0] to the correct scale for the fitness evaluation
g
the genome to scale
returns
the scaled genome

Definition Classes
Scaling
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(individuals: Seq[Individual[G, P, F]], archive: A, expression: (G, Random) ⇒ P, evaluation: (P, Random) ⇒ F)(implicit rng: Random): (Seq[Individual[G, P, F]], A)

Evolve one step
Evolve one step
individuals
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 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
Generate a population from a set of indiviuals that is filtered in a first time
individuals
a set of individual
returns
the filtred population with the meta-fitness for each individual

Definition Classes
Modifier
def toString(): String

Definition Classes
AnyRef → Any
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 Himmelblau extends problem.GAProblem with fitness.MGFitness

Type Members

abstract type A

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

type F = Seq[Double]

abstract type G

abstract type MF

type P = Seq[Double]

abstract type STATE

Abstract Value Members

abstract def breed(individuals: Seq[Individual[G, P, F]], a: A, size: Int)(implicit aprng: Random): Seq[G]

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

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

abstract def elitism(oldGeneration: Seq[Individual[G, P, F]], newGeneration: Seq[Individual[G, P, F]], archive: A)(implicit aprng: Random): Seq[Individual[G, P, F]]

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

abstract def initialArchive: A

abstract def initialState: STATE

abstract def lambda: Int

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

abstract def randomGenome(implicit rng: Random): G

abstract def terminated(population: ⇒ Population[G, P, F, MF], terminationState: STATE): (Boolean, STATE)

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

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

Concrete Value Members

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 buildRNG(seed: Long): Random

def clone(): AnyRef

final def eq(arg0: AnyRef): Boolean

def equals(arg0: Any): Boolean

def evaluate(x: Seq[Double], rng: Random): Seq[Double]

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

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

def evolve(individuals: Seq[Individual[G, P, F]], a: A, expression: (G, Random) ⇒ P, evaluation: (P, Random) ⇒ F)(implicit aprng: Random): Iterator[EvolutionState]

def express(g: G, rng: Random): List[Double]

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

def finalize(): Unit

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

def genomeSize: Int

final def getClass(): Class[_]

def hashCode(): Int

final def isInstanceOf[T0]: Boolean

def max: List[Double]

def min: List[Double]

final def ne(arg0: AnyRef): Boolean

final def notify(): Unit

final def notifyAll(): Unit

def scale(i: Individual[G, P, F]): Individual[G, P, F]

def scale[MF](i: PopulationElement[G, P, F, MF]): PopulationElement[G, P, F, MF]

def scale(g: G): G

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

def step(individuals: Seq[Individual[G, P, F]], archive: A, expression: (G, Random) ⇒ P, evaluation: (P, Random) ⇒ F)(implicit rng: Random): (Seq[Individual[G, P, F]], A)

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

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

def toString(): String

final def wait(): Unit

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

final def wait(arg0: Long): Unit

Inherited from fitness.MGFitness

Inherited from problem.GAProblem

Inherited from fitness.MG

Inherited from problem.Scaling

Inherited from GAScaling

Inherited from genome.GA

Inherited from RandomGenome

Inherited from problem.Problem

Inherited from Evolution

Inherited from elitism.Elitism

Inherited from breed.Breeding

Inherited from archive.Archive

Inherited from Lambda

Inherited from modifier.Modifier

Inherited from archive.A

Inherited from modifier.IndividualFilter

Inherited from termination.Termination

Inherited from modifier.MF