Added javadocs, made minor changes to the comments

5 files changed, 93 insertions, 132 deletions

diff --git a/src/jcgp/backend/modules/problem/DigitalCircuitProblem.java b/src/jcgp/backend/modules/problem/DigitalCircuitProblem.java
index 6654928..0071ed5 100644
--- a/src/jcgp/backend/modules/problem/DigitalCircuitProblem.java
+++ b/src/jcgp/backend/modules/problem/DigitalCircuitProblem.java
@@ -2,7 +2,6 @@ package jcgp.backend.modules.problem;
 
 import jcgp.backend.function.DigitalCircuitFunctions;
 import jcgp.backend.function.UnsignedInteger;
-import jcgp.backend.population.Chromosome;
 import jcgp.backend.population.Population;
 import jcgp.backend.resources.Resources;
 
@@ -18,7 +17,7 @@ import jcgp.backend.resources.Resources;
  *
  */
 public class DigitalCircuitProblem extends TestCaseProblem<UnsignedInteger> {
-	
+
 	/**
 	 * Construct a new instance of DigitalCircuitProblem.
 	 * 
@@ -30,43 +29,38 @@ public class DigitalCircuitProblem extends TestCaseProblem<UnsignedInteger> {
 		setName("Digital circuit");
 		setFileExtension(".plu");
 	}
-	
+
 	@Override
 	public void evaluate(Population population, Resources resources) {
 		// for every chromosome in the population
 		for (int i = 0; i < resources.populationSize(); i++) {
 			// assume an initial fitness of 0
 			int fitness = 0;
-			
+
 			// iterate over every test case
 			for (int t = 0; t < testCases.size(); t++) {
-				population.getChromosome(i).setInputs((Object[]) testCases.get(t).getInputs());
+				population.get(i).setInputs((Object[]) testCases.get(t).getInputs());
 				// check each output
 				for (int o = 0; o < resources.outputs(); o++) {
-					Integer output = ((UnsignedInteger) population.getChromosome(i).getOutput(o).calculate()).get();
+					Integer output = ((UnsignedInteger) population.get(i).getOutput(o).calculate()).get();
 					Integer matches = ~(output ^ testCases.get(t).getOutput(o).get());
 					// check only the relevant bits
 					int bits = (int) Math.pow(2.0, (double) resources.inputs());
 					for (int b = 0; b < bits; b++) {
-						if (((matches >>> b) & 1) == 1) {
-							fitness++;
-						}
+						fitness += (matches >>> b) & 1;
 					}
 				}
 			}
-			
+
 			// assign the resulting fitness to the respective individual
-			population.getChromosome(i).setFitness(fitness);
+			population.get(i).setFitness(fitness);
 		}
-		
-		// sort population
-		population.sortAscending();
 	}
-	
+
 	@Override
 	protected double getMaxFitness() {
 		// calculate the fitness by looking at inputs, not number of test cases
-		double maxFitness = Math.pow(2.0, (double) resources.inputs()) * resources.outputs();
+		double maxFitness = Math.pow(2.0, (double) getResources().inputs()) * getResources().outputs();
 		return maxFitness;
 	}
 
@@ -81,24 +75,30 @@ public class DigitalCircuitProblem extends TestCaseProblem<UnsignedInteger> {
 		for (int o = 0; o < outputCases.length; o++) {
 			outputCases[o] = new UnsignedInteger(outputs[o]);
 		}
-		
+
 		return new TestCase<UnsignedInteger>(inputCases, outputCases);
 	}
-	
+
 	@Override
-	public boolean isPerfectSolution(Chromosome fittest) {
+	public int perfectSolutionFound(Population population) {
 		// higher fitness is better
-		return fittest.getFitness() >= maxFitness.get();
+		for (int i = 0; i < getResources().populationSize(); i++) {
+			if (population.get(i).getFitness() >= maxFitness.get()) {
+				return i;
+			}
+		}
+		return -1;
 	}
 
 	@Override
-	public boolean isImprovement(Chromosome fittest) {
+	public int hasImprovement(Population population) {
 		// higher fitness is better
-		if (fittest.getFitness() > bestFitness.get()) {
-			bestFitness.set(fittest.getFitness());
-			return true;
-		} else {
-			return false;
+		for (int i = 0; i < getResources().populationSize(); i++) {
+			if (population.get(i).getFitness() > bestFitness.get()) {
+				bestFitness.set(population.get(i).getFitness());
+				return i;
+			}
 		}
+		return -1;
 	}
 }
diff --git a/src/jcgp/backend/modules/problem/Problem.java b/src/jcgp/backend/modules/problem/Problem.java
index 721b9b3..5f194b5 100644
--- a/src/jcgp/backend/modules/problem/Problem.java
+++ b/src/jcgp/backend/modules/problem/Problem.java
@@ -6,23 +6,21 @@ import jcgp.backend.function.FunctionSet;
 import jcgp.backend.modules.Module;
 import jcgp.backend.parameters.DoubleParameter;
 import jcgp.backend.parameters.monitors.DoubleMonitor;
-import jcgp.backend.population.Chromosome;
 import jcgp.backend.population.Population;
 import jcgp.backend.resources.ModifiableResources;
 import jcgp.backend.resources.Resources;
 
 /**
- * Defines the general behaviour of a CGP problem. The primary function of Problem 
+ * Defines the general behaviour of a CGP problem. The primary function of {@code Problem} 
  * is to evaluate a population and assign a fitness value to each chromosome. 
  * <br>
- * By convention, the population should be sorted into ascending order of fitness. The
- * reason for this is because high fitness is not necessarily better - some problem types
- * might treat 0 as the best fitness. In order for the evolutionary strategy to be able to
- * pick chromosomes by fitness, the safest way is to sort them such that the last chromosome
- * is the fittest.
+ * Problems are free to define whether better fitness means a higher or lower fitness value.
+ * In some problem types, it is more convenient to treat fitness 0 as the best possible value.
+ * This can be done by changing the fitness orientation to {@code BestFitness.HIGH} or {@code BestFitness.LOW} as appropriate.
+ * Fitness orientation is set to high by default.
  * <br><br>
- * When extending this class, the constructor should call a couple methods in order to
- * properly construct the problem type: {@code setFunctionSet()} and {@code setFileExtension()},
+ * When extending this class, the constructor should call a few methods in order to
+ * properly construct the problem type: {@code setFunctionSet()}, {@code setFileExtension()} and {@code setFitnessOrientation()},
  * with the respective arguments. As with all subclasses of {@code Module}, {@code setName()} and
  * {@code registerParameters()} should be used where appropriate as well.
  * <br><br>
@@ -41,12 +39,18 @@ public abstract class Problem extends Module {
 	private FunctionSet functionSet;
 	private String fileExtension = ".*";
 	
+	private BestFitness fitnessOrientation = BestFitness.HIGH;
+	
 	protected DoubleParameter maxFitness, bestFitness;
 	
 	/**
 	 * Initialises the two problem-wide parameters, maxFitness and bestFitness.
+	 *
+	 * @param resources a reference to the experiment's resources.
 	 */
-	public Problem() {
+	public Problem(Resources resources) {
+		super(resources);
+		
 		maxFitness = new DoubleMonitor(0, "Max fitness");
 		bestFitness = new DoubleMonitor(0, "Best fitness");
 		registerParameters(maxFitness, bestFitness);
@@ -73,29 +77,26 @@ public abstract class Problem extends Module {
 	public abstract void evaluate(Population population, Resources resources);
 	
 	/**
-	 * Used to assert whether a given chromosome is a perfect solution
+	 * Used to assert whether a given population contains a perfect solution
 	 * to the problem. It is up to the problem to define what qualifies
 	 * a perfect solution, as some problems (subject ones such as music and
 	 * art evolution, for example) might not have perfect solutions at all.
-	 * <br><br>
-	 * Note that if this method returns true, the experiment will move on
-	 * to the next run, or finish if no more runs are left.
 	 * 
-	 * @param candidate the potentially perfect chromosome.
-	 * @return true if the argument is a perfect solution.
+	 * @param population the population to search through for a perfect chromosome.
+	 * @return the perfect solution index, if one exits, -1 if no perfect solution was found.
 	 */
-	public abstract boolean isPerfectSolution(Chromosome candidate);
+	public abstract int perfectSolutionFound(Population population);
 	
 	/**
-	 * Used to assert whether a given chromosome is an improvement over 
+	 * Used to assert whether a given population has a chromosome that is an improvement over 
 	 * the current best chromosome. A typical implementation of this method
 	 * will simply compare chromosome fitness values, though the problem type
 	 * is free to implement this in any way.
 	 * 
-	 * @param candidate the potentially fitter chromosome.
-	 * @return true if the argument is fitter than the currently fittest chromosome.
+	 * @param population the population potentially containing a fitter chromosome.
+	 * @return the index of the first chromosome in the population that is an improvement, -1 if none is found.
 	 */
-	public abstract boolean isImprovement(Chromosome candidate);
+	public abstract int hasImprovement(Population population);
 	
 	/**
 	 * Parses the specified file and uses the parsed data to
@@ -149,6 +150,20 @@ public abstract class Problem extends Module {
 	}
 	
 	/**
+	 * @param newOrientation the new fitness orientation to set.
+	 */
+	protected void setFitnessOrientation(BestFitness newOrientation) {
+		this.fitnessOrientation = newOrientation;
+	}
+	
+	/**
+	 * @return the fitness orientation of this particular problem.
+	 */
+	public BestFitness getFitnessOrientation() {
+		return fitnessOrientation;
+	}
+	
+	/**
 	 * @return the current best fitness, in other words, the fitness
 	 * value of the fittest chromosome in the current generation.
 	 */
diff --git a/src/jcgp/backend/modules/problem/SymbolicRegressionProblem.java b/src/jcgp/backend/modules/problem/SymbolicRegressionProblem.java
index 04e9fe8..24c61d6 100644
--- a/src/jcgp/backend/modules/problem/SymbolicRegressionProblem.java
+++ b/src/jcgp/backend/modules/problem/SymbolicRegressionProblem.java
@@ -4,7 +4,6 @@ import jcgp.backend.function.SymbolicRegressionFunctions;
 import jcgp.backend.parameters.BooleanParameter;
 import jcgp.backend.parameters.DoubleParameter;
 import jcgp.backend.parameters.ParameterStatus;
-import jcgp.backend.population.Chromosome;
 import jcgp.backend.population.Population;
 import jcgp.backend.resources.Resources;
 
@@ -20,12 +19,12 @@ import jcgp.backend.resources.Resources;
  * <li>Error threshold: the maximum difference allowed between an
  * evolved output and the equivalent output from the problem data.
  * Outputs within the error threshold will be considered correct.
- * This is only used if HITS is enabled.</li>
+ * This is only used if hits is enabled.</li>
  * <li>Perfection threshold: if the fitness is calculated without
- * using the HITS method, it is a decimal value. A solution is
+ * using the hits method, it is a decimal value. A solution is
  * considered perfect when the difference between its fitness and
  * the maximum possible fitness is within the perfection threshold.</li>
- * <li>HITS-based fitness: increment the fitness by 1 whenever the
+ * <li>Hits-based fitness: increment the fitness by 1 whenever the
  * chromosome output is within the error threshold.</li></ul>
  * 
  * 
@@ -79,7 +78,7 @@ public class SymbolicRegressionProblem extends TestCaseProblem<Double> {
 			}
 		};
 		
-		hitsBasedFitness = new BooleanParameter(true, "HITS-based fitness");
+		hitsBasedFitness = new BooleanParameter(false, "Hits-based fitness");
 		
 		registerParameters(errorThreshold, perfectionThreshold, hitsBasedFitness);
 	}
@@ -87,15 +86,15 @@ public class SymbolicRegressionProblem extends TestCaseProblem<Double> {
 	@Override
 	public void evaluate(Population population, Resources resources) {
 		// for every chromosome in the population
-		for (int i = 0; i < resources.populationSize(); i++) {
+		for (int i = 0; i < getResources().populationSize(); i++) {
 			// assume an initial fitness of 0
 			double fitness = 0;
 			// for each test case
 			for (int t = 0; t < testCases.size(); t++) {
-				population.getChromosome(i).setInputs((Object[]) testCases.get(t).getInputs());
+				population.get(i).setInputs((Object[]) testCases.get(t).getInputs());
 				// check each output
-				for (int o = 0; o < resources.outputs(); o++) {
-					Double cgpValue = (Double) population.getChromosome(i).getOutput(o).calculate();
+				for (int o = 0; o < getResources().outputs(); o++) {
+					Double cgpValue = (Double) population.get(i).getOutput(o).calculate();
 					Double dataValue = testCases.get(t).getOutput(o);
 					if (hitsBasedFitness.get()) {
 						if (Math.abs(cgpValue - dataValue) <= errorThreshold.get()) {
@@ -107,11 +106,8 @@ public class SymbolicRegressionProblem extends TestCaseProblem<Double> {
 				}
 			}
 			// assign the resulting fitness to the respective individual
-			population.getChromosome(i).setFitness(fitness);
+			population.get(i).setFitness(fitness);
 		}
-		
-		// sort population
-		population.sortAscending();
 	}
 	
 	@Override
@@ -130,19 +126,27 @@ public class SymbolicRegressionProblem extends TestCaseProblem<Double> {
 	}
 
 	@Override
-	public boolean isPerfectSolution(Chromosome fittest) {
+	public int perfectSolutionFound(Population population) {
 		// higher fitness is better
-		return fittest.getFitness() >= maxFitness.get() - perfectionThreshold.get();
+		for (int i = 0; i < getResources().populationSize(); i++) {
+			if (population.get(i).getFitness() >= maxFitness.get() - perfectionThreshold.get()) {
+				return i;
+			}
+		}
+		return -1;
 	}
 	
 	@Override
-	public boolean isImprovement(Chromosome fittest) {
+	public int hasImprovement(Population population) {
 		// higher fitness is better
-		if (fittest.getFitness() > bestFitness.get()) {
-			bestFitness.set(fittest.getFitness());
-			return true;
-		} else {
-			return false;
+		for (int i = 0; i < getResources().populationSize(); i++) {
+			System.out.println("checking for improvement");
+			if (population.get(i).getFitness() > bestFitness.get()) {
+				System.out.println("found a better chr, " + i);
+				bestFitness.set(population.get(i).getFitness());
+				return i;
+			}
 		}
+		return -1;
 	}
 }
diff --git a/src/jcgp/backend/modules/problem/TestCaseProblem.java b/src/jcgp/backend/modules/problem/TestCaseProblem.java
index c11fab4..69c078d 100644
--- a/src/jcgp/backend/modules/problem/TestCaseProblem.java
+++ b/src/jcgp/backend/modules/problem/TestCaseProblem.java
@@ -30,7 +30,7 @@ public abstract class TestCaseProblem<T> extends Problem {
 	 * contains arrays of inputs and outputs and associated getters.
 	 * 
 	 * @author Eduardo Pedroni
-	 * @param <U>
+	 * @param <U> the data type of the test case.
 	 */
 	public static class TestCase<U> {
 		private U[] inputs;
@@ -80,7 +80,6 @@ public abstract class TestCaseProblem<T> extends Problem {
 	}
 	
 	protected ObservableList<TestCase<T>> testCases;
-	protected Resources resources;
 	
 	/**
 	 * Creates a new TestCaseProblem object.
@@ -88,8 +87,7 @@ public abstract class TestCaseProblem<T> extends Problem {
 	 * @param resources a reference to the experiment's resources.
 	 */
 	public TestCaseProblem(Resources resources) {
-		super();
-		this.resources = resources;
+		super(resources);
 		testCases = FXCollections.observableArrayList();
 	}
 	
@@ -139,12 +137,12 @@ public abstract class TestCaseProblem<T> extends Problem {
 	public final void addTestCase(String[] inputs, String[] outputs) {
 		TestCase<T> testCase = parseTestCase(inputs, outputs);
 		
-		if (testCase.getInputs().length != resources.inputs()) {
+		if (testCase.getInputs().length != getResources().inputs()) {
 			throw new IllegalArgumentException("Received test case with " + testCase.getInputs().length + 
-					" inputs but need exactly " + resources.inputs());
-		} else if (testCase.getOutputs().length != resources.outputs()) {
+					" inputs but need exactly " + getResources().inputs());
+		} else if (testCase.getOutputs().length != getResources().outputs()) {
 			throw new IllegalArgumentException("Received test case with " + testCase.getOutputs().length + 
-					" outputs but need exactly " + resources.outputs());
+					" outputs but need exactly " + getResources().outputs());
 		} else {
 			this.testCases.add(testCase);
 			maxFitness.set(getMaxFitness());
diff --git a/src/jcgp/backend/modules/problem/TravellingSalesmanProblem.java b/src/jcgp/backend/modules/problem/TravellingSalesmanProblem.java
deleted file mode 100644
index 94417ae..0000000
--- a/src/jcgp/backend/modules/problem/TravellingSalesmanProblem.java
+++ /dev/null
@@ -1,56 +0,0 @@
-package jcgp.backend.modules.problem;
-
-import java.io.File;
-
-import jcgp.backend.function.TravellingSalesmanFunctions;
-import jcgp.backend.population.Chromosome;
-import jcgp.backend.population.Population;
-import jcgp.backend.resources.ModifiableResources;
-import jcgp.backend.resources.Resources;
-
-/**
- * Travelling salesman problem
- * <br><br>
- * Using this problem type, travelling salesman tours can be evolved.
- * {@code parseData()} must be used to load the desired city
- * coordinates in the standard .tsp format. 
- * 
- * @see TravellingSalesmanFunctions
- * @author Eduardo Pedroni
- *
- */
-public class TravellingSalesmanProblem extends Problem {
-	
-	/**
-	 * Construct a new instance of TravellingSalesmanProblem.
-	 * 
-	 * @param resources a reference to the experiment's resources.
-	 */
-	public TravellingSalesmanProblem(Resources resources) {
-		setFunctionSet(new TravellingSalesmanFunctions());
-		setName("Travelling salesman");
-		setFileExtension(".tsp");
-	}
-
-	@Override
-	public void evaluate(Population population, Resources resources) {
-		// TODO Auto-generated method stub
-	}
-
-	@Override
-	public boolean isPerfectSolution(Chromosome fittest) {
-		// TODO Auto-generated method stub
-		return false;
-	}
-
-	@Override
-	public void parseProblemData(File file, ModifiableResources resources) {
-		// TODO Auto-generated method stub
-	}
-
-	@Override
-	public boolean isImprovement(Chromosome fittest) {
-		// TODO Auto-generated method stub
-		return false;
-	}
-}