axle.ml.GeneticAlgorithm

GeneticAlgorithmC

class GeneticAlgorithmC[G <: HList, Z <: HList] extends AnyRef

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Instance Constructors

  1. new GeneticAlgorithmC(populationSize: Int = 1000, numGenerations: Int = 100)(implicit species: Species[G], zipper: Aux[::[G, ::[G, HNil]], Z], mapperMix: Mapper[Mixer.type, Z], mapperMutate: Mapper[Mutator.type, Z])

Value Members

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

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

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  3. final def ##(): Int

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  4. final def ==(arg0: AnyRef): Boolean

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

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  6. final def asInstanceOf[T0]: T0

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  7. def clone(): AnyRef

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    protected[java.lang]
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    @throws( ... )

  8. def crossover[Z <: HList](h1: G, h2: G)(implicit zipper: Aux[::[G, ::[G, HNil]], Z], mapper: Mapper[Mixer.type, Z]): Out

    There are many variations of produceChild.

    There are many variations of produceChild. The important components are:

    1. Fitness-based selection 2. Crossover / gene-swapping 3. Mutation

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

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  10. def equals(arg0: Any): Boolean

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  11. def finalize(): Unit

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

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  13. def hashCode(): Int

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  14. def initialPopulation(): IndexedSeq[(G, Double)]

  15. final def isInstanceOf[T0]: Boolean

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  16. def live(population: IndexedSeq[(G, Double)], fitnessLog: List[(Double, Double, Double)]): (IndexedSeq[(G, Double)], List[(Double, Double, Double)])

  17. def minMaxAve(population: IndexedSeq[(G, Double)]): (Double, Double, Double)

  18. def mutate[Z <: HList](x: G, r: G)(implicit zipper: Aux[::[G, ::[G, HNil]], Z], mapper: Mapper[Mutator.type, Z]): Out

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

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  20. final def notify(): Unit

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  21. final def notifyAll(): Unit

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  22. def run(): GeneticAlgorithmLog[G]

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

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  24. def toString(): String

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  25. final def wait(): Unit

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

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

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