fr.iscpif.mgo.algorithm

BehaviourSearch

trait BehaviourSearch extends GAProblem with NoFitness with HitMapArchive with GeneticBreeding with SortedTournamentSelection with IdentityCrossOver with TournamentOnRank with RankModifier with HierarchicalRanking with HitCountModifiedFitness with RandomNicheElitism with CounterTermination with CoEvolvingSigmaValuesMutation with GAGenomeWithSigma

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Inherited
  1. BehaviourSearch
  2. GAGenomeWithSigma
  3. GAGenomeWithSigmaType
  4. CoEvolvingSigmaValuesMutation
  5. MutationRate
  6. Sigma
  7. CounterTermination
  8. RandomNicheElitism
  9. NicheElitism
  10. MergedGenerations
  11. HitCountModifiedFitness
  12. HierarchicalRanking
  13. TournamentOnRank
  14. RankModifier
  15. ModifiedFitness
  16. RankMF
  17. Ranking
  18. IdentityCrossover
  19. SortedTournamentSelection
  20. Tournament
  21. GeneticBreeding
  22. Mutation
  23. Crossover
  24. Selection
  25. HitMapArchive
  26. Niche
  27. NoFitness
  28. GAProblem
  29. MG
  30. Scaling
  31. GAScaling
  32. GA
  33. RandomGenome
  34. Problem
  35. Evolution
  36. Elitism
  37. Breeding
  38. Archive
  39. Lambda
  40. Modifier
  41. A
  42. IndividualFilter
  43. Termination
  44. MF
  45. F
  46. P
  47. G
  48. AnyRef
  49. Any
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Type Members

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

    Definition Classes
    HitMapArchiveArchiveA
  2. 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
  3. type F = None.type

    Definition Classes
    NoFitnessF
  4. type G = Genome

    Definition Classes
    GAGenomeWithSigmaTypeG
  5. type MF = Rank

    the type of the meta-fitness

    the type of the meta-fitness

    Definition Classes
    RankModifierMF
  6. abstract type NICHE

    Definition Classes
    Niche
  7. abstract type P

    Definition Classes
    P
  8. 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
    CounterTerminationTermination

Abstract Value Members

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

    Definition Classes
    Problem
  2. abstract def genomeSize: Int

    Size of the value part of the genome

    Size of the value part of the genome

    Definition Classes
    GAProblemGA
  3. abstract def lambda: Int

    the size of the offspring

    the size of the offspring

    Definition Classes
    Lambda
  4. abstract def max: Seq[Double]

    Maximum scaled value in the correct order

    Maximum scaled value in the correct order

    Definition Classes
    Scaling
  5. abstract def min: Seq[Double]

    minimum scaled value in the correct order

    minimum scaled value in the correct order

    Definition Classes
    Scaling
  6. abstract def niche(individual: Individual[G, P, F]): NICHE

    Definition Classes
    Niche
  7. abstract def steps: Int

    Number of steps before the algorithm stops

    Number of steps before the algorithm stops

    Definition Classes
    CounterTermination

Concrete Value Members

  1. final def !=(arg0: Any): Boolean

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

    Definition Classes
    AnyRef → Any
  3. final def ==(arg0: Any): Boolean

    Definition Classes
    AnyRef → Any
  4. def archive(a: A, individuals: Seq[Individual[G, P, F]]): A

    Definition Classes
    Archive
  5. final def asInstanceOf[T0]: T0

    Definition Classes
    Any
  6. def breed(individuals: Seq[Individual[G, P, F]], a: A, size: Int)(implicit rng: Random): Seq[G]

    Breed genomes from a population

    Breed genomes from a population

    individuals

    the population from which genomes are breeded

    size

    the size of the breeded set

    returns

    the breeded genomes

    Definition Classes
    GeneticBreedingBreeding
  7. def buildRNG(seed: Long): Random

    Definition Classes
    Evolution
  8. def clone(): AnyRef

    Attributes
    protected[java.lang]
    Definition Classes
    AnyRef
    Annotations
    @throws( ... )
  9. def cloneProbability: Double

    Definition Classes
    GeneticBreeding
  10. def combine(a1: A, a2: A): A

    Definition Classes
    HitMapArchiveArchive
  11. def crossover(g1: G, g2: G, population: Seq[Individual[G, P, F]], archive: A)(implicit rng: Random): IndexedSeq[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
    IdentityCrossoverCrossover
  12. def diff(original: A, modified: A): A

    Definition Classes
    HitMapArchiveArchive
  13. def elitism(individuals: Seq[Individual[G, P, F]], a: A)(implicit rng: Random): Seq[Individual[G, P, F]]

    Reduce the number of elements of the population and return a new one

    Reduce the number of elements of the population and return a new one

    individuals

    the population to shrink

    returns

    the shrinked population

    Definition Classes
    NicheElitismMergedGenerations
  14. 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
    MergedGenerationsElitism
  15. final def eq(arg0: AnyRef): Boolean

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

    Definition Classes
    AnyRef → Any
  17. def evaluate(phenotype: P, rng: Random): F

    Evaluate a phenotype

    Evaluate a phenotype

    phenotype

    the phenotype to evaluate

    returns

    the phenotype

    Definition Classes
    NoFitnessProblem
  18. def evolve(implicit rng: Random): Iterator[EvolutionState]

    Definition Classes
    ScalingProblem
  19. 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
  20. 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
  21. 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
  22. def finalize(): Unit

    Attributes
    protected[java.lang]
    Definition Classes
    AnyRef
    Annotations
    @throws( classOf[java.lang.Throwable] )
  23. def fitness: SimpleLens[F, Seq[Double]]

    Definition Classes
    NoFitnessMG
  24. def fitnesses(evaluated: Seq[Individual[G, P, F]], archive: A): Seq[Seq[Double]]

  25. def genome: SimpleLens[G, Seq[Double]]

    The entire genome with potentially additional information

    The entire genome with potentially additional information

    Definition Classes
    GAGenomeWithSigmaGA
  26. final def getClass(): Class[_]

    Definition Classes
    AnyRef → Any
  27. def hashCode(): Int

    Definition Classes
    AnyRef → Any
  28. def hits(a: A, c: NICHE): Int

    Definition Classes
    HitMapArchive
  29. def initialArchive: scala.collection.Map[NICHE, Int]

    Definition Classes
    HitMapArchiveArchive
  30. def initialState: Int

    Compute the initial state

    Compute the initial state

    returns

    the initial state

    Definition Classes
    CounterTerminationTermination
  31. final def isInstanceOf[T0]: Boolean

    Definition Classes
    Any
  32. def keep(individuals: Seq[Individual[G, P, F]])(implicit rng: Random): Seq[Individual[G, P, F]]

    Definition Classes
    RandomNicheElitismNicheElitism
  33. def minimumSigma: Double

  34. def modify(evaluated: 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

    returns

    the population with the meta-fitness for each individual

    Definition Classes
    RankModifierModifier
  35. def mutate(genome: G, population: Seq[Individual[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
    CoEvolvingSigmaValuesMutationMutation
  36. def mutationRate: Double

    Definition Classes
    MutationRate
  37. final def ne(arg0: AnyRef): Boolean

    Definition Classes
    AnyRef
  38. def nicheSize: Int

    Definition Classes
    RandomNicheElitism
  39. final def notify(): Unit

    Definition Classes
    AnyRef
  40. final def notifyAll(): Unit

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

    Definition Classes
    GAGenomeWithSigmaGARandomGenome
  42. def rank(values: Seq[Seq[Double]]): 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
    HierarchicalRankingRanking
  43. def rank: Lens[MF, MF, Lazy[Int], Lazy[Int]]

    Definition Classes
    RankModifierRankMF
  44. def scale(i: Individual[G, P, F]): Individual[G, P, F]

    Definition Classes
    Scaling
  45. 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
  46. 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
  47. 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
    ScalingGAScaling
  48. def selection(population: Population[G, P, F, MF])(implicit rng: Random): Iterator[Individual[G, P, F]]

    Select an individual among the population.

    Select an individual among the population.

    population

    the population in which selection occurs

    returns

    the selected individual

    Definition Classes
    SortedTournamentSelectionSelection
  49. def sigma: Lens[G, G, Seq[Double], Seq[Double]]

    Definition Classes
    GAGenomeWithSigmaSigma
  50. 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
  51. final def synchronized[T0](arg0: ⇒ T0): T0

    Definition Classes
    AnyRef
  52. def terminated(population: ⇒ Population[G, P, F, MF], step: STATE): (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
    CounterTerminationTermination
  53. def toArchive(individuals: Seq[Individual[G, P, F]]): A

    Definition Classes
    HitMapArchiveArchive
  54. 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
  55. def toString(): String

    Definition Classes
    AnyRef → Any
  56. def tournament(e1: PopulationElement[G, P, F, MF], e2: PopulationElement[G, P, F, MF])(implicit rng: Random): PopulationElement[G, P, F, MF]

    Select the best ranked and if equal the more diverse individual between two population elements.

    Select the best ranked and if equal the more diverse individual between two population elements.

    e1

    the first population element

    e2

    the second population element

    returns

    the winning population element

    Definition Classes
    TournamentOnRankTournament
  57. def values: Lens[G, G, Seq[Double], 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
    GAGenomeWithSigmaGA
  58. final def wait(): Unit

    Definition Classes
    AnyRef
    Annotations
    @throws( ... )
  59. final def wait(arg0: Long, arg1: Int): Unit

    Definition Classes
    AnyRef
    Annotations
    @throws( ... )
  60. final def wait(arg0: Long): Unit

    Definition Classes
    AnyRef
    Annotations
    @throws( ... )

Inherited from genome.GAGenomeWithSigma

Inherited from GAGenomeWithSigmaType

Inherited from MutationRate

Inherited from genome.Sigma

Inherited from elitism.NicheElitism

Inherited from MergedGenerations

Inherited from modifier.RankModifier

Inherited from ModifiedFitness

Inherited from modifier.RankMF

Inherited from ranking.Ranking

Inherited from IdentityCrossover

Inherited from Tournament

Inherited from breed.GeneticBreeding

Inherited from mutation.Mutation

Inherited from Crossover

Inherited from selection.Selection

Inherited from archive.HitMapArchive

Inherited from niche.Niche

Inherited from problem.NoFitness

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

Inherited from fitness.F

Inherited from phenotype.P

Inherited from genome.G

Inherited from AnyRef

Inherited from Any

Ungrouped