PSE

Type Members

type A = scala.collection.Map[NICHE, Int]

Definition Classes
HitMapArchive → Archive → A
type Crossover = (G, G, Population[G, P, F], A, Random) ⇒ Seq[G]

Definition Classes
Crossover
type Evaluation = Int

Definition Classes
TournamentOnHitCount → 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 = None.type

Definition Classes
NoFitness → F
type FILTER = (Population[G, P, F]) ⇒ Population[G, P, F]

Definition Classes
IndividualFilter
type G = Genome

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

Definition Classes
Tournament
type Mutation = (G, Population[G, P, F], A, Random) ⇒ G

Definition Classes
Mutation
type NICHE = Seq[Int]

Definition Classes
PhenotypeGridNiche → Niche
type P = Seq[Double]

Definition Classes
DoubleSeqPhenotype → P
type STATE = Int

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
CounterTermination → Termination

Abstract Value Members

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 gridSize: Seq[Double]

Definition Classes
PhenotypeGridNiche
abstract def lambda: Int

the size of the offspring
the size of the offspring

Definition Classes
Lambda
abstract def steps: Int

Number of steps before the algorithm stops
Number of steps before the algorithm stops

Definition Classes
CounterTermination

Concrete Value Members

final def !=(arg0: Any): Boolean

Definition Classes
AnyRef → Any
final def ##(): Int

Definition Classes
AnyRef → Any
def +(other: String): String

Implicit information
This member is added by an implicit conversion from PSE to any2stringadd[PSE] performed by method any2stringadd in scala.Predef.
Definition Classes
any2stringadd
def ->[B](y: B): (PSE, B)

Implicit information
This member is added by an implicit conversion from PSE to ArrowAssoc[PSE] performed by method ArrowAssoc in scala.Predef.
Definition Classes
ArrowAssoc
Annotations
@inline()
final def ==(arg0: Any): Boolean

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

Definition Classes
HitMapArchive → 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], archive: A)(implicit rng: Random): Seq[G]

Definition Classes
GeneticBreeding
def breed(population: Population[G, P, F], archive: 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: Lens[G, Seq[Double]]): Lens[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 combine(a1: A, a2: A): A

Definition Classes
HitMapArchive
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
DynamicCrossover → Crossover
def crossoverExploration: Double

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

Definition Classes
DynamicCrossover
def crossovers: Vector[(G, G, Population[G, P, F], A, Random) ⇒ Seq[G]]

Definition Classes
DynamicGACrossover → Crossover
def doubleSeq: Lens[P, Seq[Double]]

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

Definition Classes
Elitism
def ensuring(cond: (PSE) ⇒ Boolean, msg: ⇒ Any): PSE

Implicit information
This member is added by an implicit conversion from PSE to Ensuring[PSE] performed by method Ensuring in scala.Predef.
Definition Classes
Ensuring
def ensuring(cond: (PSE) ⇒ Boolean): PSE

Implicit information
This member is added by an implicit conversion from PSE to Ensuring[PSE] performed by method Ensuring in scala.Predef.
Definition Classes
Ensuring
def ensuring(cond: Boolean, msg: ⇒ Any): PSE

Implicit information
This member is added by an implicit conversion from PSE to Ensuring[PSE] performed by method Ensuring in scala.Predef.
Definition Classes
Ensuring
def ensuring(cond: Boolean): PSE

Implicit information
This member is added by an implicit conversion from PSE to Ensuring[PSE] performed by method Ensuring in scala.Predef.
Definition Classes
Ensuring
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
TournamentOnHitCount → Tournament
def evaluate(phenotype: P, rng: Random): F

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

Definition Classes
NoFitness → Problem
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], archive: 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
archive
the initial archive
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 filters: Seq[FILTER]

Definition Classes
IndividualFilter
def finalize(): Unit

Attributes
protected[java.lang]
Definition Classes
AnyRef
Annotations
@throws( classOf[java.lang.Throwable] )
def fitness(f: F): Seq[Nothing]

Definition Classes
NoFitness → MG
def fitness(individual: Individual[G, P, F]): Seq[Double]

Definition Classes
MG
def formatted(fmtstr: String): String

Implicit information
This member is added by an implicit conversion from PSE to StringFormat[PSE] performed by method StringFormat in scala.Predef.
Definition Classes
StringFormat
Annotations
@inline()
def fromCrossover: Lens[G, Option[Int]]

Definition Classes
GAGenomeWithSigma → DynamicApplication
def fromMutation: Lens[G, Option[Int]]

Definition Classes
GAGenomeWithSigma
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 hits(a: A, c: NICHE): Int

Definition Classes
HitMapArchive
def initialArchive(implicit rng: Random): scala.collection.Map[NICHE, Int]

Definition Classes
HitMapArchive → Archive
def initialState: Int

Compute the initial state
Compute the initial state
returns
the initial state

Definition Classes
CounterTermination → Termination
final def isInstanceOf[T0]: Boolean

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

Definition Classes
RandomNicheElitism → 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 generator
returns
the mutated genome

Definition Classes
DynamicMutation → Mutation
def mutationExploration: Double

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

Definition Classes
DynamicMutation
def mutations: Vector[(G, Population[G, P, F], A, Random) ⇒ G]

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

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

Definition Classes
PhenotypeGridNiche → Niche
def nicheSize: Int

Definition Classes
RandomNicheElitism
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])(implicit rng: Random): 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: Lens[G, Seq[Double]]

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

Definition Classes
ProportionalNumberOfRound → NumberOfRound
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 selectionPressure: Double

Definition Classes
ProportionalNumberOfRound
def sigma: Lens[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 terminated(population: Population[G, P, F], step: STATE)(implicit rng: Random): (Boolean, STATE)

Test if the algorithm has converged.
Test if the algorithm has converged.
population
the current population
returns
a boolean which is equal to true if a terminal state has been detected and the new termination state

Definition Classes
CounterTermination → Termination
def toArchive(population: Population[G, P, F]): A

Definition Classes
HitMapArchive
def toString(): String

Definition Classes
AnyRef → Any
def tournament(e1: IndividualEvaluation, e2: IndividualEvaluation)(implicit rng: Random): IndividualEvaluation

Definition Classes
TournamentOnHitCount → Tournament
def values: Lens[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( ... )
def →[B](y: B): (PSE, B)

Implicit information
This member is added by an implicit conversion from PSE to ArrowAssoc[PSE] performed by method ArrowAssoc in scala.Predef.
Definition Classes
ArrowAssoc

Related Doc: package algorithm

Type Members

type A = scala.collection.Map[NICHE, Int]

type Crossover = (G, G, Population[G, P, F], A, Random) ⇒ Seq[G]

type Evaluation = Int

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 = None.type

type FILTER = (Population[G, P, F]) ⇒ Population[G, P, F]

type G = Genome

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

type Mutation = (G, Population[G, P, F], A, Random) ⇒ G

type NICHE = Seq[Int]

type P = Seq[Double]

type STATE = Int

Abstract Value Members

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

abstract def genomeSize: Int

abstract def gridSize: Seq[Double]

abstract def lambda: Int

abstract def steps: Int

Concrete Value Members

final def !=(arg0: Any): Boolean

final def ##(): Int

def +(other: String): String

def ->[B](y: B): (PSE, B)

final def ==(arg0: Any): Boolean

def archive(archive: 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], archive: A)(implicit rng: Random): Seq[G]

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

def buildRNG(seed: Long): Random

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

def clone(): AnyRef

def cloneProbability: Double

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

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 crossoverExploration: Double

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

def crossovers: Vector[(G, G, Population[G, P, F], A, Random) ⇒ Seq[G]]

def doubleSeq: Lens[P, Seq[Double]]

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

def ensuring(cond: (PSE) ⇒ Boolean, msg: ⇒ Any): PSE

def ensuring(cond: (PSE) ⇒ Boolean): PSE

def ensuring(cond: Boolean, msg: ⇒ Any): PSE

def ensuring(cond: Boolean): PSE

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

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], archive: 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 filters: Seq[FILTER]

def finalize(): Unit

def fitness(f: F): Seq[Nothing]

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

def formatted(fmtstr: String): String

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

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

def genomesEqualOn(g: G): Any

final def getClass(): Class[_]

def hashCode(): Int

def hits(a: A, c: NICHE): Int

def initialArchive(implicit rng: Random): scala.collection.Map[NICHE, Int]

def initialState: Int

final def isInstanceOf[T0]: Boolean

def keep(population: 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

def mutationExploration: Double

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

def mutations: Vector[(G, Population[G, P, F], A, Random) ⇒ G]

final def ne(arg0: AnyRef): Boolean

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

def nicheSize: Int

final def notify(): Unit

final def notifyAll(): Unit