1 files changed, 31 insertions, 24 deletions

diff --git a/src/jcgp/JCGP.java b/src/jcgp/JCGP.java
index 70b01a8..a19e085 100644
--- a/src/jcgp/JCGP.java
+++ b/src/jcgp/JCGP.java
@@ -1,10 +1,14 @@
 package jcgp;
 
+import java.util.ArrayList;
+
 import jcgp.backend.modules.ea.EvolutionaryAlgorithm;
 import jcgp.backend.modules.ea.MuPlusLambda;
 import jcgp.backend.modules.ea.TournamentSelection;
-import jcgp.backend.modules.fitness.FitnessFunction;
-import jcgp.backend.modules.fitness.testcase.TestCaseEvaluator.TestCase;
+import jcgp.backend.modules.fitness.DigitalCircuit;
+import jcgp.backend.modules.fitness.Problem;
+import jcgp.backend.modules.fitness.SymbolicRegression;
+import jcgp.backend.modules.fitness.TestCaseProblem.TestCase;
 import jcgp.backend.modules.mutator.Mutator;
 import jcgp.backend.modules.mutator.PointMutator;
 import jcgp.backend.population.Population;
@@ -50,30 +54,34 @@ public class JCGP {
 			new TournamentSelection()};
 	private EvolutionaryAlgorithm evolutionaryAlgorithm;
 	
-	// fitness evaluators
-	private FitnessFunction[] fitnessFunctions = new FitnessFunction[] {
-			/*new TestCaseEvaluator()*/ };
-	private FitnessFunction fitnessFunction;
+	// problem types
+	private Problem[] problems = new Problem[] {
+			new SymbolicRegression(),
+			new DigitalCircuit() };
+	private Problem problem;
 
 	/*
 	 * the population of chromosomes
 	 */
 	private Population population;
-
 	private boolean finished = false;
 	
 	public JCGP() {
+		setEvolutionaryAlgorithm(0);
+		setMutator(0);
+		setProblem(0);
 		
 		population = new Population(resources);
-
-		evolutionaryAlgorithm = evolutionaryAlgorithms[0];
 		
-		mutator = mutators[0];
+		ArrayList<TestCase<Integer>> tc = new ArrayList<TestCase<Integer>>();
+		tc.add(new TestCase<Integer>(new Integer[]{1, 2, 3}, new Integer[]{-4, 5, 6}));
 		
-		//fitnessFunction = fitnessFunctions[0];
+		((SymbolicRegression) problem).setTestCases(tc);
 		
-		//resources.setTestCases(new TestCase(new Integer[]{1, 2, 3}, new Integer[]{-4, 5, 6}));
-
+		ArrayList<TestCase<Integer>> tcdc = new ArrayList<TestCase<Integer>>();
+		tcdc.add(new TestCase<Integer>(new Integer[]{1, 2, 3}, new Integer[]{-4, 5, 6}));
+		
+		((DigitalCircuit) problems[1]).setTestCases(tc);
 	}
 
 	public ModifiableResources getResources() {
@@ -119,16 +127,16 @@ public class JCGP {
 	/**
 	 * @return the fitnessFunctions
 	 */
-	public FitnessFunction[] getFitnessFunctions() {
-		return fitnessFunctions;
+	public Problem[] getProblems() {
+		return problems;
 	}
 
 
 	/**
 	 * @return the fitnessFunction
 	 */
-	public FitnessFunction getFitnessFunction() {
-		return fitnessFunction;
+	public Problem getProblem() {
+		return problem;
 	}
 	
 	
@@ -149,21 +157,21 @@ public class JCGP {
 
 
 	/**
-	 * @param fitnessFunction the fitnessFunction to set
+	 * @param problem the fitnessFunction to set
 	 */
-	public void setFitnessFunction(int index) {
-		this.fitnessFunction = fitnessFunctions[index];
+	public void setProblem(int index) {
+		this.problem = problems[index];
+		resources.setFunctionSet(problem.getFunctionSet());
 	}
 	
 	public void nextGeneration() {
 		if (!finished) {
-			fitnessFunction.evaluate(population, (Resources) resources);
-
+			problem.evaluate(population, (Resources) resources);
 			report();
 
 			if (resources.getInt("currentGen") < resources.getInt("generations")) {
 				// we still have generations left to go
-				if (population.getChromosome(evolutionaryAlgorithm.getFittestChromosome()).getFitness() >= resources.getInt("maxFitness")) {
+				if (problem.isPerfectSolution(population.getChromosome(evolutionaryAlgorithm.getFittestChromosome()))) {
 					// solution has been found, start next run
 					resources.println("Solution found on generation " + resources.getInt("currentGen") + ", chromosome: " + evolutionaryAlgorithm.getFittestChromosome());
 					
@@ -181,7 +189,6 @@ public class JCGP {
 				} else {
 					resources.set("currentGen", resources.getInt("currentGen") + 1);
 				}
-
 			} else {
 				// the run has ended, check if any more runs must be done
 				resources.println("Solution not found, highest fitness achieved was "