mgo.test.TestOptimumDiversity.scala Maven / Gradle / Ivy

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///*
// * Copyright (C) 13/11/13 Romain Reuillon
// *
// * This program is free software: you can redistribute it and/or modify
// * it under the terms of the GNU Affero General Public License as published by
// * the Free Software Foundation, either version 3 of the License, or
// * (at your option) any later version.
// *
// * This program is distributed in the hope that it will be useful,
// * but WITHOUT ANY WARRANTY; without even the implied warranty of
// * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
// * GNU Affero General Public License for more details.
// *
// * You should have received a copy of the GNU General Public License
// * along with this program.  If not, see .
// */
//
//package mgo.test
//
//import mgo.evolution._
//import monocle.syntax._
//import scala.util.Random
//import scalax.io.Resource
//
//object TestOptimumDiversity extends App {
//
//  /* trait OptimumDiversity <: Evolution
//    with BinaryTournamentSelection
//    with TournamentOnRankAndDiversity
//    with NonDominatedElitism
//    with DynamicApplicationGA
//    with DiversityRanking
//    with FitnessCrowdingDiversity
//    with CeilDiversityRanking
//    with MaxAggregation
//    with NonStrictDominance
//    with NoArchive
//    with CloneRemoval
//    with GeneticBreeding
//    with MGFitness
//    with ClampedGenome*/
//
//  /*trait OptimumDiversity <: NoArchive
//    with BestRankedNicheElitism
//    with ParetoRanking
//    with GAGenotypeGridNiche
//    with MG
//    with FitnessCrowdingDiversity
//    with GeneticBreeding
//    with BinaryTournamentSelection
//    with TournamentOnRankAndDiversity
//    with DynamicApplicationGA
//    with NonStrictDominance
//    with ClampedGenome
//    with ProportionalNumberOfRound
//
//  val m = new Rastrigin with OptimumDiversity with CounterTermination {
//    def genomeSize: Int = 2
//    def lambda: Int = 200
//    //def mu: Int = 200
//    def steps = 1000
//    def gridSize = Seq(0.25, 0.25)
//  }
//
//  implicit val rng = new Random
//
//  m.evolve.untilConverged { s =>
//    val output = Resource.fromFile(s"/tmp/novelty/novelty${s.generation}.csv")
//    s.population.foreach {
//      i => output.append((m.scale(i.genome &|-> m.values get)).mkString(",") + "," + i.fitness.mkString(",") + "\n")
//    }
//  }*/
//
//  /*val m = new ZDT4 with OptimumDiversity with CounterTermination {
//    def genomeSize: Int = 2
//    def lambda: Int = 200
//    def steps = 400
//    def gridSize = Seq(0.1, 0.5, 0.5)
//  }
//
//  implicit val rng = new Random
//
//  m.evolve.untilConverged {
//    s =>
//      val output = Resource.fromFile(s"/tmp/novelty/novelty${s.generation}.csv")
//      s.population.foreach {
//        i => output.append((m.scale(i.genome &|-> m.values get)).mkString(",") + "," + i.fitness.mkString(",") + "\n")
//      }
//      //println(s.individuals.map(_.fitness))
//
//      //import Ordering.Implicits._
//      //println(s.individuals.map(m.niche).sorted.mkString("\n"))
//
//      //println(s.individuals.map(i => m.niche(i).mkString(",")).mkString("\n"))
//
//      //println(s.individuals.size)
//      println(s.generation)
//  }*/
//
//}