Finish refactor

assignement.md

@@ -12,8 +12,9 @@ static classes.
 
 ### Prepare support for class inheritance
 
-## Limit number of generations
+---
 
+## Limit number of generations
 
 
 ## Roulette selection

src/main/java/Main.java

@@ -1,5 +1,6 @@
 import domain.annealing.SimulatedAnnealing;
 import domain.ant_colony.AntColonyOptimization;
+import domain.binary.algorithm.CustomGeneticAlgorithm;
 import domain.binary.algorithm.SimpleGeneticAlgorithm;
 
 import java.util.Scanner;
@@ -19,13 +20,14 @@ public class Main {
                 break;
             case 2:
                 SimpleGeneticAlgorithm ga = new SimpleGeneticAlgorithm(
+                        50,
                         0.5,
                         0.025,
                         5,
                         true,
-                        new byte[64]
+                        "1011000100000100010000100000100111001000000100000100000000001111"
                 );
-                ga.runAlgorithm(50, "1011000100000100010000100000100111001000000100000100000000001111");
+                ga.launch();
                 break;
             case 3:
                 AntColonyOptimization antColony = new AntColonyOptimization(21);

src/main/java/domain/binary/Population.java

@@ -42,8 +42,8 @@ public class Population {
         return individuals[index];
     }
 
-    public Individual setIndividual(int index, Individual individual) {
+    public void setIndividual(int index, Individual individual) {
-        return individuals[index] = individual;
+        individuals[index] = individual;
     }
 
     public Individual getFittest() {

src/main/java/domain/binary/algorithm/AbstractGeneticAlgorithm.java

@@ -0,0 +1,42 @@
+package domain.binary.algorithm;
+
+import lombok.Data;
+
+@Data
+public abstract class AbstractGeneticAlgorithm {
+
+    private byte[] solution;
+    private int generationCount;
+
+    public AbstractGeneticAlgorithm(String solution) {
+        generationCount = 0;
+        this.solution = mapSolution(solution);
+    }
+
+    public void launch(){
+        while(!stopCriterion()){
+            run();
+            generationCount++;
+        }
+    }
+
+    protected abstract void run();
+    protected abstract boolean stopCriterion();
+
+    protected int getMaxFitness() {
+        return solution.length;
+    }
+
+    private byte[] mapSolution(String newSolution) {
+        solution = new byte[newSolution.length()];
+        for (int i = 0; i < newSolution.length(); i++) {
+            String character = newSolution.substring(i, i + 1);
+            if (character.contains("0") || character.contains("1")) {
+                solution[i] = Byte.parseByte(character);
+            } else {
+                solution[i] = 0;
+            }
+        }
+        return solution;
+    }
+}

src/main/java/domain/binary/algorithm/CustomGeneticAlgorithm.java

@@ -1,27 +1,24 @@
 package domain.binary.algorithm;
 
-import domain.binary.Population;
-
 public class CustomGeneticAlgorithm extends SimpleGeneticAlgorithm {
 
+    private final int maxGenerationCount;
+
     public CustomGeneticAlgorithm(
+            int popSize,
+            int maxGenerationCount,
             double uniformRate,
             double mutationRate,
             int tournamentSize,
             boolean elitism,
-            byte[] solution
+            String solution
     ) {
-        super(uniformRate, mutationRate, tournamentSize, elitism, solution);
+        super(popSize, uniformRate, mutationRate, tournamentSize, elitism, solution);
+        this.maxGenerationCount = maxGenerationCount;
     }
 
     @Override
-    public boolean runAlgorithm(int populationSize, String solution){
+    protected boolean stopCriterion() {
-        return false;
+        return getGenerationCount() == maxGenerationCount;
     }
-
-    @Override
-    public boolean checkStopCriteria(Population pop) {
-        return false;
-    }
-
 }

src/main/java/domain/binary/algorithm/SimpleGeneticAlgorithm.java

@@ -1,81 +1,41 @@
 package domain.binary.algorithm;
 
-import domain.binary.Individual;
 import domain.binary.Population;
 import lombok.Data;
 
-@Data
+public class SimpleGeneticAlgorithm extends AbstractGeneticAlgorithm{
-public class SimpleGeneticAlgorithm {
 
-    private final double uniformRate;
+    private Population population;
-    private final double mutationRate;
-    private final int tournamentSize;
     private final boolean elitism;
-    private byte[] solution;
 
     public SimpleGeneticAlgorithm(
+            int popSize,
             double uniformRate,
             double mutationRate,
             int tournamentSize,
             boolean elitism,
-            byte[] solution
+            String solution
     ){
-        this.uniformRate = uniformRate;
+        super(solution);
-        this.mutationRate = mutationRate;
+        this.population = new Population(
-        this.tournamentSize = tournamentSize;
+                popSize,
-        this.elitism = elitism;
-        this.solution = solution;
-    }
-
-    public boolean runAlgorithm(int populationSize, String solution) {
-        setSolution(solution);
-        Population myPop = new Population(
-                populationSize,
                 uniformRate,
                 mutationRate,
                 tournamentSize,
-                this.solution);
+                getSolution());
-
+        this.elitism = elitism;
-        int generationCount = 1;
-
-        while (checkStopCriteria(myPop)) {
-            System.out.println("Generation: " + generationCount + " Correct genes found: " + myPop.getFittest().getFitness());
-            myPop = myPop.evolve(elitism, this.solution);
-            generationCount++;
-        }
-
-        System.out.println("Solution found!");
-        System.out.println("Generation: " + generationCount);
-        System.out.println("Genes: ");
-        System.out.println(myPop.getFittest());
-        return true;
     }
 
-    public boolean checkStopCriteria(Population pop) {
+    @Override
-        int fitness = pop.getFittest().getFitness();
+    protected void run() {
+        System.out.println("Generation: " + getGenerationCount() + " Correct genes found: " + population.getFittest().getFitness());
+        population = population.evolve(elitism, getSolution());
+    }
+
+    @Override
+    protected boolean stopCriterion() {
+        int fitness = population.getFittest().getFitness();
         int maxFitness = getMaxFitness();
-        return fitness < maxFitness;
+        return fitness >= maxFitness;
     }
-
-    protected int getMaxFitness() {
-        int maxFitness = solution.length;
-        return maxFitness;
-    }
-
-    protected void setSolution(String newSolution) {
-        if (newSolution.length() != this.solution.length) {
-            throw new RuntimeException("The solution needs to have " + this.solution.length + " bytes");
-        }
-
-        solution = new byte[newSolution.length()];
-        for (int i = 0; i < newSolution.length(); i++) {
-            String character = newSolution.substring(i, i + 1);
-            if (character.contains("0") || character.contains("1")) {
-                solution[i] = Byte.parseByte(character);
-            } else {
-                solution[i] = 0;
-            }
-        }
-    }
-
 }