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fr.iscpif.mgo

breeding

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object breeding

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  1. type Breeding[M[_], I, G] = Kleisli[M, Vector[I], Vector[G]]

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  2. type Crossover[M[_], P, O] = Kleisli[M, P, O]

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    A crossover is a function from some individuals (parents or mates, can be a single individual, a pair or a vector) to one or more genomes (even if more than one, all genomes are coming from crossing over the same parents).

    A crossover is a function from some individuals (parents or mates, can be a single individual, a pair or a vector) to one or more genomes (even if more than one, all genomes are coming from crossing over the same parents).

    The type P represent the parents, typically a single individual, a tuple, or a vector.

    The type G can also represent more than one genome.

  3. type Mutation[M[_], G1, G2] = Kleisli[M, G1, G2]

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    A mutation is a function from a single genome to another single genome

Value Members

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

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  2. final def ##(): Int

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  3. final def ==(arg0: Any): Boolean

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  4. object Breeding

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  5. object Crossover

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  6. object Mutation

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  7. final def asInstanceOf[T0]: T0

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  8. def bgaM[M[_]](mutationRate: (Int) ⇒ Double, mutationRange: Double)(implicit MM: Monad[M], MR: RandomGen[M]): Mutation[M, Vector[Double], Vector[Double]]

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  9. def binaryTournament[M[_], I, K](rounds: (Int) ⇒ Int = _ => 1)(implicit arg0: Monad[M], arg1: RandomGen[M], arg2: Order[K]): Kleisli[M, Vector[(I, K)], I]

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  10. def blxC[M[_]](alpha: Double = 0.5)(implicit arg0: Monad[M], arg1: RandomGen[M]): Crossover[M, (Vector[Double], Vector[Double]), Vector[Double]]

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  11. def breed[OI, OO](tournament: Kleisli[[β]IndexedStateT[[+X]X, Random, Random, β], Vector[OI], OI], op: Kleisli[[β]IndexedStateT[[+X]X, Random, Random, β], (OI, OI), Vector[OO]], size: Int): Kleisli[[β]IndexedStateT[[+X]X, Random, Random, β], Vector[OI], Vector[OO]]

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    ** Dynamic breeding ***

  12. def clone(): AnyRef

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  13. def clonesReplace[M[_], I, G](cloneProbability: Double, population: Vector[I], genome: (I) ⇒ G)(implicit MM: Monad[M], MR: RandomGen[M]): Breeding[M, G, G]

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    Randomly replaces some of the genomes in gs by genomes taken from the original population of I

  14. final def eq(arg0: AnyRef): Boolean

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  15. def equals(arg0: Any): Boolean

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  16. def finalize(): Unit

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  17. final def getClass(): Class[_]

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  18. def groupConsecutive[M[_], I](groupSize: Int)(implicit arg0: Monad[M]): Breeding[M, I, Vector[I]]

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    ** Mating ***

  19. def hashCode(): Int

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  20. def identityC[M[_], I](implicit arg0: Monad[M]): Crossover[M, I, I]

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  21. final def isInstanceOf[T0]: Boolean

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  22. final def ne(arg0: AnyRef): Boolean

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  23. final def notify(): Unit

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  24. final def notifyAll(): Unit

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  25. def pairConsecutive[M[_], I](implicit arg0: Monad[M]): Breeding[M, I, (I, I)]

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  26. def replicatePairC[M[_], P, O](c: Crossover[M, P, O])(implicit arg0: Monad[M]): Crossover[M, P, (O, O)]

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  27. def sbxC[M[_]](distributionIndex: Double = 2.0)(implicit MM: Monad[M], MR: RandomGen[M]): Crossover[M, (Vector[Double], Vector[Double]), (Vector[Double], Vector[Double])]

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    SBX RGA operator with Bounded Variable modification, see APPENDIX A p30 into :

    SBX RGA operator with Bounded Variable modification, see APPENDIX A p30 into :

    http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.33.7291&rep=rep1&type=pdf

    INPROCEEDINGS{Deb98anefficient, author = {Kalyanmoy Deb}, title = {An Efficient Constraint Handling Method for Genetic Algorithms}, booktitle = {Computer Methods in Applied Mechanics and Engineering}, year = {1998}, pages = {311--338} }

    Notes : Deb implementation differs from NSGA2 he proposed on this site : http://www.iitk.ac.in/kangal/codes.shtml

    Implementation based on http://repository.ias.ac.in/9415/1/318.pdf

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

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  29. def toString(): String

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  30. def tournament[M[_], I, K](ranking: Kleisli[M, Vector[I], Vector[K]], size: Int, rounds: (Int) ⇒ Int = _ => 1)(implicit arg0: Order[K], MM: Monad[M], MR: RandomGen[M]): Breeding[M, I, I]

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    ** Selection ***

  31. final def wait(): Unit

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

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  33. final def wait(arg0: Long): Unit

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