AggregatedOptimisation

Type Members

type A = None.type

Definition Classes
NoArchive → Archive → A
type Ancestors = (F, F)

Definition Classes
DynamicApplicationGAGenome
trait CandidateCrossover extends Crossover

Definition Classes
DynamicApplicationGA
trait CandidateMutation extends Mutation

Definition Classes
DynamicApplicationGA
trait DAMethodInjection extends AnyRef

Definition Classes
DynamicApplicationGA
type Evaluation = None.type

Definition Classes
TournamentOnAggregatedFitness → Tournament
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
DynamicApplicationGAGenome → G
case class Genome(values: Seq[Double], sigma: Seq[Double], ancestors: Option[Ancestors] = None, mutation: Option[Int] = None, crossover: Option[Int] = None) extends Product with Serializable

Definition Classes
DynamicApplicationGAGenome
type IndividualEvaluation = (Individual[G, P, F], Evaluation)

Definition Classes
Tournament
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 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 mu: Int

the size of the population
the size of the population

Definition Classes
Mu
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
lazy val adaptiveCauchy: AdaptiveCauchyMutation with CandidateMutation with DAMethodInjection

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

Definition Classes
MaxAggregation → Aggregation
def ancestors: Lens[G, G, Option[Ancestors], Option[Ancestors]]

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

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

Definition Classes
Any
lazy val bga: BGAMutation with CandidateMutation with DAMethodInjection

Definition Classes
DynamicApplicationGA
lazy val bigbga: BGAMutation with CandidateMutation with DAMethodInjection

Definition Classes
DynamicApplicationGA
lazy val blx: BLXCrossover with CandidateCrossover with DAMethodInjection

Definition Classes
DynamicApplicationGA
def breed(i1: Individual[G, P, F], i2: Individual[G, P, F], population: Population[G, P, F], a: A)(implicit rng: Random): Seq[G]

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

Breed genomes from a population
Breed genomes from a population
population
the population from which genomes are breeded
size
the size of the breeded set
returns
the breeded genomes

Definition Classes
GeneticBreeding → 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 cloneProbability: Double

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

Definition Classes
BestAggregatedElitism → Elitism
def crossover(g1: G, g2: G, population: Population[G, P, F], archive: A)(implicit rng: Random): Seq[G]

Crossover g1 and g2
Crossover g1 and g2
g1
a genome
g2
another genome
population
last computed population
archive
last archive
returns
the result of the crossover

Definition Classes
DynamicApplicationGA → Crossover
def crossover: Lens[G, G, Option[Int], Option[Int]]

Definition Classes
DynamicApplicationGAGenome
def crossoverStats(p: Population[G, P, F]): scala.collection.Map[Int, Double]

Definition Classes
DynamicApplicationGA
def crossovers: Seq[CandidateCrossover]

Definition Classes
DynamicApplicationGA
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(population: Population[G, P, F], archive: A)(implicit rng: Random): Seq[Evaluation]

Definition Classes
TournamentOnAggregatedFitness → Tournament
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
CloneRemoval → 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
def genomesEqualOn(g: G): Any

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

Definition Classes
AnyRef → Any
def hashCode(): Int

Definition Classes
AnyRef → Any
lazy val idc: IdentityCrossOver with CandidateCrossover with DAMethodInjection

Definition Classes
DynamicApplicationGA
def initialArchive(implicit rng: Random): A

Definition Classes
NoArchive → Archive
final def isInstanceOf[T0]: Boolean

Definition Classes
Any
def mutate(g: G, population: Population[G, P, F], archive: A)(implicit rng: Random): G

Mutate a genome
Mutate a genome
population
the last computed population
archive
the last archive
rng
a random number geneartor
returns
the mutated genome

Definition Classes
DynamicApplicationGA → Mutation
def mutation: Lens[G, G, Option[Int], Option[Int]]

Definition Classes
DynamicApplicationGAGenome
def mutationStats(p: Population[G, P, F]): scala.collection.Map[Int, Double]

Definition Classes
DynamicApplicationGA
def mutations: Seq[CandidateMutation]

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

Definition Classes
AnyRef
final def notify(): Unit

Definition Classes
AnyRef
final def notifyAll(): Unit

Definition Classes
AnyRef
def operatorExploration: Double

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

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

Definition Classes
DynamicApplicationGAGenome → GA
def rounds(population: Population[G, P, F], archive: A): Int

Definition Classes
BinaryTournamentSelection → NumberOfRound
lazy val sbx: SBXCrossover with CandidateCrossover with DAMethodInjection

Definition Classes
DynamicApplicationGA
def selection(population: Population[G, P, F], archive: A)(implicit rng: Random): Iterator[Individual[G, P, F]]

Select an individual among the population.
Select an individual among the population.
param population the population in which selection occurs
returns
the selected individual

Definition Classes
BinaryTournamentSelection → Selection
def sigma: Lens[G, G, Seq[Double], Seq[Double]]

Definition Classes
DynamicApplicationGAGenome → 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 tournament(e1: IndividualEvaluation, e2: IndividualEvaluation)(implicit rng: Random): IndividualEvaluation

Definition Classes
TournamentOnAggregatedFitness → Tournament
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 AggregatedOptimisation extends Evolution with DynamicApplicationGA with BinaryTournamentSelection with TournamentOnAggregatedFitness with BestAggregatedElitism with NoArchive with CloneRemoval with GeneticBreeding with MGFitness with ClampedGenome with MaxAggregation

Type Members

type A = None.type

type Ancestors = (F, F)

trait CandidateCrossover extends Crossover

trait CandidateMutation extends Mutation

trait DAMethodInjection extends AnyRef

type Evaluation = None.type

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

case class Genome(values: Seq[Double], sigma: Seq[Double], ancestors: Option[Ancestors] = None, mutation: Option[Int] = None, crossover: Option[Int] = None) extends Product with Serializable

type IndividualEvaluation = (Individual[G, P, F], Evaluation)

abstract type P

abstract type STATE

Abstract Value Members

abstract def genomeSize: Int

abstract def initialState: STATE

abstract def lambda: Int

abstract def mu: 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

lazy val adaptiveCauchy: AdaptiveCauchyMutation with CandidateMutation with DAMethodInjection

def aggregate(f: F): Double

def ancestors: Lens[G, G, Option[Ancestors], Option[Ancestors]]

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

final def asInstanceOf[T0]: T0

lazy val bga: BGAMutation with CandidateMutation with DAMethodInjection

lazy val bigbga: BGAMutation with CandidateMutation with DAMethodInjection

lazy val blx: BLXCrossover with CandidateCrossover with DAMethodInjection

def breed(i1: Individual[G, P, F], i2: Individual[G, P, F], population: Population[G, P, F], a: A)(implicit rng: Random): Seq[G]

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 cloneProbability: Double

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

def crossover(g1: G, g2: G, population: Population[G, P, F], archive: A)(implicit rng: Random): Seq[G]

def crossover: Lens[G, G, Option[Int], Option[Int]]

def crossoverStats(p: Population[G, P, F]): scala.collection.Map[Int, Double]

def crossovers: Seq[CandidateCrossover]

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(population: Population[G, P, F], archive: A)(implicit rng: Random): Seq[Evaluation]

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(f: F): F

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

def genomesEqualOn(g: G): Any

final def getClass(): Class[_]

def hashCode(): Int

lazy val idc: IdentityCrossOver with CandidateCrossover with DAMethodInjection

def initialArchive(implicit rng: Random): A

final def isInstanceOf[T0]: Boolean

def mutate(g: G, population: Population[G, P, F], archive: A)(implicit rng: Random): G

def mutation: Lens[G, G, Option[Int], Option[Int]]

def mutationStats(p: Population[G, P, F]): scala.collection.Map[Int, Double]

def mutations: Seq[CandidateMutation]

final def ne(arg0: AnyRef): Boolean

final def notify(): Unit

final def notifyAll(): Unit

def operatorExploration: Double

def randomGenome(implicit rng: Random): Genome

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

def rounds(population: Population[G, P, F], archive: A): Int

lazy val sbx: SBXCrossover with CandidateCrossover with DAMethodInjection

def selection(population: Population[G, P, F], archive: A)(implicit rng: Random): Iterator[Individual[G, P, F]]

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 tournament(e1: IndividualEvaluation, e2: IndividualEvaluation)(implicit rng: Random): IndividualEvaluation

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