Map

Type Members

type A = None.type

Definition Classes
NoArchive → 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
GAGenomeWithSigma → G
type NICHE = (Int, Int)

Definition Classes
MapNiche → Niche
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 plot(individual: Individual[G, P, F]): (Int, Int)

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

Definition Classes
Any
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
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
NicheElitism → 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
SBXCrossover → Crossover
def distributionIndex: Double

distribution index parameter of the algorithm
distribution index parameter of the algorithm

Definition Classes
SBXCrossover
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 hashCode(): Int

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

Definition Classes
NoArchive → Archive
def isDominated(p1: F, p2: F): Boolean

Definition Classes
StrictDominance → Dominance
final def isInstanceOf[T0]: Boolean

Definition Classes
Any
def keep(individuals: Population[G, P, F])(implicit rng: Random): Population[G, P, F]

Definition Classes
BestAggregatedNicheElitism → NicheElitism
def minimumSigma: Double

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

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

Definition Classes
AdaptiveCauchyMutation → Mutation
final def ne(arg0: AnyRef): Boolean

Definition Classes
AnyRef
def neighbourPressure: Int

Definition Classes
MapSelection
def niche(individual: Individual[G, P, F]): (Int, Int)

Definition Classes
MapNiche → Niche
def nicheSize: Int

Definition Classes
MapNiche
final def notify(): Unit

Definition Classes
AnyRef
final def notifyAll(): Unit

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

Definition Classes
GAGenomeWithSigma → GA → RandomGenome
def rank(values: Population[G, P, F]): Seq[Lazy[Int]]

Compute the rank of a set of individuals.
Compute the rank of a set of individuals.
values
the values to rank
returns
the ranks of the individuals in the same order

Definition Classes
HierarchicalRanking → Ranking
def rawValues: SimpleLens[G, Seq[Double]]

Definition Classes
GAGenomeWithSigma → GA
def sbxCrossover(g1: G, g2: G)(implicit rng: Random): Seq[G]

Definition Classes
SBXCrossover
def selection(population: Population[G, P, F], a: 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
MapSelection → Selection
def sigma: SimpleLens[G, Seq[Double]]

Definition Classes
GAGenomeWithSigma → 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 tournamentSize: Int

Definition Classes
MapSelection
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 Map extends Evolution with MG with NoArchive with MapSelection with GAGenomeWithSigma with MaxAggregation with SBXCrossover with BestAggregatedNicheElitism with MapNiche with AdaptiveCauchyMutation with HierarchicalRanking with StrictDominance with GeneticBreeding with ClampedGenome

Type Members

type A = 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

abstract type F

type G = Genome

type NICHE = (Int, Int)

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 plot(individual: Individual[G, P, F]): (Int, 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 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(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 distributionIndex: Double

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 hashCode(): Int

def initialArchive(implicit rng: Random): A

def isDominated(p1: F, p2: F): Boolean

final def isInstanceOf[T0]: Boolean

def keep(individuals: Population[G, P, F])(implicit rng: Random): Population[G, P, F]

def minimumSigma: Double

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

final def ne(arg0: AnyRef): Boolean

def neighbourPressure: Int

def niche(individual: Individual[G, P, F]): (Int, Int)

def nicheSize: Int

final def notify(): Unit

final def notifyAll(): Unit

def randomGenome(implicit rng: Random): Genome

def rank(values: Population[G, P, F]): Seq[Lazy[Int]]

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

def sbxCrossover(g1: G, g2: G)(implicit rng: Random): Seq[G]

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

def sigma: 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 tournamentSize: Int

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.GeneticBreeding

Inherited from dominance.StrictDominance

Inherited from dominance.Dominance

Inherited from ranking.HierarchicalRanking

Inherited from ranking.Ranking

Inherited from mutation.AdaptiveCauchyMutation

Inherited from MinimumSigma

Inherited from mutation.Mutation

Inherited from niche.MapNiche