mgo.evolution.algorithm.NoisyNichedNSGA2.scala Maven / Gradle / Ivy

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//package mgo.evolution.algorithm
//
//import scala.language.higherKinds
//import mgo.evolution._
//import mgo.ranking._
//import mgo.evolution.breeding._
//import mgo.evolution.elitism._
//import mgo.evolution.contexts._
//import tools._
//import cats._
//import cats.data._
//import cats.implicits._
//import GenomeVectorDouble._
//import freedsl.dsl
//import freedsl.tool._
//import monocle.macros._
//
//object noisyNichedNSGA2 extends niche.Imports {
//
//  import algorithm.noisynsga2._
//
//  def elitism[M[_]: cats.Monad: Random: Generation, N](mu: Int, historySize: Int, aggregation: Vector[Vector[Double]] => Vector[Double], niche: Individual => N): Elitism[M, Individual] =
//    noisyNichedNSGA2Operations.elitism[M, Individual, N](
//      vectorFitness,
//      aggregation,
//      (Individual.genome composeLens vectorValues).get,
//      Individual.age,
//      Individual.historyAge)(niche, mu, historySize)
//
//  def result(population: Vector[Individual], aggregation: Vector[Vector[Double]] => Vector[Double], scaling: Vector[Double] => Vector[Double]) =
//    noisynsga2.result(population, aggregation, scaling)
//
//  object NoisyNSGA2 {
//
//    def run[T](rng: util.Random)(f: contexts.run.Implicits => T): T = contexts.run(rng)(f)
//    def run[T](state: EvolutionState[Unit])(f: contexts.run.Implicits => T): T = contexts.run(state)(f)
//
//    implicit def isAlgorithm[M[_]: Generation: Random: cats.Monad: StartTime, N]: Algorithm[NoisyNichedNSGA2[N], M, Individual, Genome, EvolutionState[Unit]] = new Algorithm[NoisyNichedNSGA2[N], M, Individual, Genome, EvolutionState[Unit]] {
//      def initialPopulation(t: NoisyNichedNSGA2[N]) =
//        stochasticInitialPopulation[M, Genome, Individual](
//          noisynsga2.initialGenomes[M](t.lambda, t.genomeSize),
//          noisynsga2.expression(t.fitness))
//
//      def step(t: NoisyNichedNSGA2[N]): Kleisli[M, Vector[Individual], Vector[Individual]] =
//        noisynsga2Operations.step[M, Individual, Genome](
//          noisynsga2.adaptiveBreeding[M](t.lambda, t.operatorExploration, t.cloneProbability, t.aggregation),
//          noisynsga2.expression(t.fitness),
//          elitism[M, N](t.mu, t.historySize, t.aggregation, t.niche))
//
//      def state = noisynsga2.state[M]
//    }
//  }
//
//  case class NoisyNichedNSGA2[N](
//    niche: Individual => N,
//    mu: Int,
//    lambda: Int,
//    fitness: (util.Random, Vector[Double]) => Vector[Double],
//    aggregation: Vector[Vector[Double]] => Vector[Double],
//    genomeSize: Int,
//    historySize: Int = 100,
//    cloneProbability: Double = 0.2,
//    operatorExploration: Double = 0.1)
//
//}
//
//object noisyNichedNSGA2Operations {
//
//  def elitism[M[_]: cats.Monad: Random: Generation, I, N](
//    history: monocle.Lens[I, Vector[Vector[Double]]],
//    aggregation: Vector[Vector[Double]] => Vector[Double],
//    values: I => Vector[Double],
//    age: monocle.Lens[I, Long],
//    historyAge: monocle.Lens[I, Long])(niche: I => N, mu: Int, historySize: Int): Elitism[M, I] = Elitism[M, I] { population =>
//
//    def nicheElitism(population: Vector[I]) =
//      noisynsga2Operations.elitism[M, I](
//        history,
//        aggregation,
//        values,
//        age,
//        historyAge)(mu, historySize).apply(population)
//
//    byNiche(population, nicheElitism, niche)
//  }
//
//}