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GeneticAlgo

object GeneticAlgo

1) start with an initial population, represented as Seq[A] 2) sort on fitness 3) if there is a solution, good times, else ... 4) reproduce - "mate" each entry with another within a certain proximity. we will end up with another Seq[A] with the offspring 5) potentially mutate some records - again go from Seq[A] -> Seq[A]

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Type Members

  1. type Generation[A] = (Int, IndexedSeq[Geneology[A]])

Value Members

  1. final def !=(arg0: Any): Boolean
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  2. final def ##: Int
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  6. def combineAndMutate[A](random: Seed, mom: Geneology[A], dad: Geneology[A], generation: Int, childRecordId: Int)(implicit arg0: AlgoSettings[A]): (Seed, Geneology[A])

    Do the mating/mutating of the two records

    Do the mating/mutating of the two records

    random

    our 'random' seed used to control randomness in our function for determining how to mate the two parents

    mom

    the parent record 1

    dad

    another parent record

    generation

    some detail for the geneology

    childRecordId

    some detail for the geneology

  7. def createNextGeneration[A](originalPopulation: IndexedSeq[Geneology[A]], initialSeed: Seed, generation: Int)(implicit arg0: AlgoSettings[A]): (Seed, IndexedSeq[Geneology[A]])

    This is our application logic - take some existing population (represented as Geneology so as to carry on a history)

    This is our application logic - take some existing population (represented as Geneology so as to carry on a history)

    generation

    a generation value to use when creating the new elements

  8. final def eq(arg0: AnyRef): Boolean
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  14. final def ne(arg0: AnyRef): Boolean
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  15. final def notify(): Unit
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  16. final def notifyAll(): Unit
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  17. def solve[A](population: Seq[A], seed: Seed = Seed(), debug: (Generation[A]) => Unit)(implicit arg0: AlgoSettings[A]): Option[Geneology[A]]
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