/*
    * Licensed to the Apache Software Foundation (ASF) under one or more
    * contributor license agreements.  See the NOTICE file distributed with
    * this work for additional information regarding copyright ownership.
    * The ASF licenses this file to You under the Apache License, Version 2.0
    * (the "License"); you may not use this file except in compliance with
    * the License.  You may obtain a copy of the License at
    *
    *      http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   */
  package org.apache.commons.math4.legacy.genetics;
  
  
  import java.util.LinkedList;
  import java.util.List;
  
  import org.junit.Assert;
  import org.junit.Test;
  
  
  public class FitnessCachingTest {
  
      // parameters for the GA
      private static final int DIMENSION = 50;
      private static final double CROSSOVER_RATE = 1;
      private static final double MUTATION_RATE = 0.1;
      private static final int TOURNAMENT_ARITY = 5;
  
      private static final int POPULATION_SIZE = 10;
      private static final int NUM_GENERATIONS = 50;
      private static final double ELITISM_RATE = 0.2;
  
      // how many times was the fitness computed
      private static int fitnessCalls = 0;
  
  
      @Test
      public void testFitnessCaching() {
          // initialize a new genetic algorithm
          GeneticAlgorithm ga = new GeneticAlgorithm(
                  new OnePointCrossover<>(),
                  CROSSOVER_RATE, // all selected chromosomes will be recombined (=crosssover)
                  new BinaryMutation(),
                  MUTATION_RATE, // no mutation
                  new TournamentSelection(TOURNAMENT_ARITY)
          );
  
          // initial population
          Population initial = randomPopulation();
          // stopping conditions
          StoppingCondition stopCond = new FixedGenerationCount(NUM_GENERATIONS);
  
          // run the algorithm
          ga.evolve(initial, stopCond);
  
          int neededCalls =
              POPULATION_SIZE /*initial population*/ +
              (NUM_GENERATIONS - 1) /*for each population*/ * (int)(POPULATION_SIZE * (1.0 - ELITISM_RATE)) /*some chromosomes are copied*/
              ;
          Assert.assertTrue(fitnessCalls <= neededCalls); // some chromosomes after crossover may be the same os old ones
      }
  
  
      /**
       * Initializes a random population.
       */
      private static ElitisticListPopulation randomPopulation() {
          List<Chromosome> popList = new LinkedList<>();
  
          for (int i=0; i<POPULATION_SIZE; i++) {
              BinaryChromosome randChrom = new DummyCountingBinaryChromosome(BinaryChromosome.randomBinaryRepresentation(DIMENSION));
              popList.add(randChrom);
          }
          return new ElitisticListPopulation(popList, popList.size(), ELITISM_RATE);
      }
  
      private static final class DummyCountingBinaryChromosome extends DummyBinaryChromosome {
  
          DummyCountingBinaryChromosome(List<Integer> representation) {
              super(representation);
          }
  
          @Override
          public double fitness() {
              fitnessCalls++;
              return 0;
          }
      }
  }