Added more comments, minor refactorings

10 files changed, 467 insertions, 105 deletions

diff --git a/src/jcgp/backend/population/Chromosome.java b/src/jcgp/backend/population/Chromosome.java
index d23f43c..b99b817 100644
--- a/src/jcgp/backend/population/Chromosome.java
+++ b/src/jcgp/backend/population/Chromosome.java
@@ -4,6 +4,54 @@ import java.util.ArrayList;
 
 import jcgp.backend.resources.Resources;
 
+/**
+ * This class encapsulates a CGP chromosome.
+ * <br><br>
+ * A chromosome contains a matrix of nodes and arrays of inputs and outputs.
+ * These elements are all interconnected, and actually form the chromosome
+ * network itself. Individual nodes can be retrieved using {@code getNode()}
+ * which requires the row and column to be specified. The same works for
+ * inputs and outputs using the associated getters, in which case only the
+ * index is necessary.
+ * <br><br>
+ * In evolutionary computation it is often necessary to make copies of 
+ * chromosomes; this can be accomplished in JCGP in two ways. The recommended
+ * way to do this is using {@code copyChromosome()} in {@code Population}, but alternatively
+ * it can be done by using the {@code Chromosome} copy constructor and specifying the
+ * object to copy from, or by using the {@code copyGenes()} method. 
+ * <br><br>
+ * To illustrate this, given two chromosomes, chr1 and chr2, the following code:
+ * <br><br>
+ * <code>
+ * chr1.copyGenes(chr2);
+ * </code><br><br>
+ * will modify all of chr1's connections and functions to match those of chr2, without
+ * creating a new instance. In contrast, 
+ * <br><br>
+ * <code>
+ * chr1 = new Chromosome(chr2);
+ * </code><br><br>
+ * creates a new instance of chromosome which is identical to chr2 and assigns it to chr1,
+ * meaning any old references to chr1 that are not updated will still refer to a chromosome
+ * that is not identical to chr2. In practice, the most reliable way is to use the copy method
+ * in {@code Population}. Assuming chr1 and chr2 are indexed 1 and 2 in {@code population} respectively,
+ * <br><br>
+ * population.copyChromosome(2, 1);
+ * <br><br>
+ * will copy chr2 into chr1 without creating new instances or requiring access to the underlying
+ * chromosome array. {@code Chromosome} offers a variety of methods to compare chromosomes as well, 
+ * such as {@code compareGenesTo()} and {@code compareActiveGenesTo()}. {@code Comparable} is implemented
+ * to compare fitness value, meaning {@code compareTo()} returns a value depending the relative fitness
+ * of the compared chromosomes.
+ * <br><br>
+ * In order to set the chromosome's input values for decoding, {@code setInputs()} should be used. A few 
+ * utility methods are provided in order to retrieve random elements from the chromosome, which are used
+ * internally to initialise with random connections but also externally by mutators when performing 
+ * mutations.
+ * 
+ * @author Eduardo Pedroni
+ *
+ */
 public class Chromosome implements Comparable<Chromosome> {
 	
 	private Resources resources;
@@ -19,14 +67,13 @@ public class Chromosome implements Comparable<Chromosome> {
 
 	/**
 	 * Initialise a chromosome with the specified parameters. Random valid connections
-	 * are created.
-	 * 
-	 * 
+	 * are created upon initialisation.
+	 *
+	 * @param resources the experiment's resources.
 	 */
 	public Chromosome(Resources resources) {
 		// store a reference to the parameters
 		this.resources = resources;
-		
 		// allocate memory for all elements of the chromosome
 		instantiateElements();
 		// set random connections so that the chromosome can be evaluated
@@ -38,12 +85,11 @@ public class Chromosome implements Comparable<Chromosome> {
 	 * 
 	 * Initialise a new chromosome with the exact same connections as a given instance of Chromosome.
 	 * 
-	 * @param clone the chromosome to be copied
+	 * @param clone the chromosome to be copied.
 	 */
 	public Chromosome(Chromosome clone) {
 		// store a reference to the parameters
 		this.resources = clone.getResources();
-		
 		// allocate memory for all elements of the chromosome
 		instantiateElements();
 		// initialise all connections based on argument
@@ -51,7 +97,14 @@ public class Chromosome implements Comparable<Chromosome> {
 	}
 
 	/**
-	 *
+	 * Allocates the necessary memory for all of the nodes, inputs
+	 * and outputs in the chromosome according to the experiment
+	 * resources. 
+	 * <br>
+	 * Note that this does not actually initialise the genes, as it
+	 * is not possible to do so until they have all been initialised;
+	 * to initialise the genes, {@code reinitialiseConnections()} should
+	 * be used.
 	 */
 	private void instantiateElements() {
 		inputs = new Input[(resources.inputs())];
@@ -75,7 +128,9 @@ public class Chromosome implements Comparable<Chromosome> {
 	}
 
 	/**
-	 * 
+	 * Sets random connections and functions across the entire
+	 * chromosome. This method can be used more than once for
+	 * each instance, if entirely random chromosomes are desired.
 	 */
 	public void reinitialiseConnections() {
 
@@ -92,6 +147,7 @@ public class Chromosome implements Comparable<Chromosome> {
 			}
 		}
 
+		// set random outputs
 		for (Output output : outputs) {
 			output.setConnection(0, getRandomConnection());
 		}
@@ -99,11 +155,21 @@ public class Chromosome implements Comparable<Chromosome> {
 	}
 
 	/**
-	 * @param clone
+	 * Creates a deep copy of the specified chromosome in the
+	 * this instance. In practice, this iterates through the
+	 * entire chromosome making equivalent connections and
+	 * setting functions to the same values as those in the
+	 * specified chromosome.
+	 * <br>
+	 * It is assumed that both chromosomes have the same 
+	 * topology; while this method will still run if that is not
+	 * the case, the effects might be undesirable and null pointer
+	 * access might occur.
+	 * 
+	 * @param clone the chromosome to clone.
 	 */
 	public void copyGenes(Chromosome clone) {
 		int arity = resources.arity();
-		
 		// copy nodes - [rows][columns]
 		for (int r = 0; r < nodes.length; r++) {
 			for (int c = 0; c < nodes[r].length; c++) {
@@ -141,31 +207,64 @@ public class Chromosome implements Comparable<Chromosome> {
 		}
 	}
 
+	/**
+	 * Returns a reference to any node, addressed by row and column.
+	 * 
+	 * @param row the row of the node.
+	 * @param column the column of the node.
+	 * @return the addressed node.
+	 */
 	public Node getNode(int row, int column) {
 		return nodes[row][column];
 	}
 
+	/**
+	 * Returns a reference to the indexed output.
+	 * 
+	 * @param index the output index.
+	 * @return the output reference.
+	 */
 	public Output getOutput(int index) {
 		return outputs[index];
 	}
 
+	/**
+	 * Returns a reference to the indexed input.
+	 * 
+	 * @param index the input index.
+	 * @return the input reference.
+	 */
 	public Input getInput(int index) {
 		return inputs[index];
 	}
 
+	/**
+	 * @return the fitness of the chromosome.
+	 */
 	public double getFitness() {
 		return fitness;
 	}
 
+	/**
+	 * Sets the fitness of the chromosome. This method
+	 * should be used by the experiment problem when the
+	 * population is evaluated in order to assign a fitness
+	 * to each individual.
+	 * 
+	 * @param newFitness the fitness to assign.
+	 */
 	public void setFitness(double newFitness) {
 		fitness = newFitness;
 	}
 
 	/**
+	 * Loops through the inputs and sets the specified values,
+	 * so that evaluations can be performed. If the number of
+	 * elements in the array of values does not match the
+	 * number of inputs exactly, an exception is thrown.
 	 * 
-	 * 
-	 * @param values
-	 * @throws ParameterMismatchException
+	 * @param values the values the input should take.
+	 * @throws ParameterMismatchException if the wrong number of values is received.
 	 */
 	public void setInputs(Object ... values) {
 		// if the values provided don't match the specified number of inputs, the user should be warned
@@ -180,9 +279,11 @@ public class Chromosome implements Comparable<Chromosome> {
 	}
 
 	/**
-	 * This method is useful for mutating chromosomes.
+	 * This method is useful for mutating chromosomes. It returns any
+	 * random {@code MutableElement} out of the chromosome with equal
+	 * probability. 
 	 * 
-	 * @return a random element that can be mutated - Node or Output
+	 * @return a random element that can be mutated - node or output.
 	 */
 	public MutableElement getRandomMutableElement() {
 		// choose output or node
@@ -199,12 +300,12 @@ public class Chromosome implements Comparable<Chromosome> {
 	}
 
 	/**
-	 * Returns a random allowed connection respecting levels back.</br>
+	 * Returns a random allowed connection respecting levels back.<br>
 	 * This method may always pick inputs, as they can be picked
 	 * regardless of the column.
 	 * 
-	 * @param column the column to use as reference
-	 * @return a random connection
+	 * @param column the column to use as reference.
+	 * @return a random connection.
 	 */
 	public Connection getRandomConnection(int column) {
 		// work out the allowed range obeying levels back
@@ -226,12 +327,9 @@ public class Chromosome implements Comparable<Chromosome> {
 
 	/**
 	 * This method will pick a completely random connection, independently
-	 * of levels back, including inputs.
-	 * 
-	 * Useful for setting outputs.
+	 * of levels back, including inputs. It is useful for setting outputs.
 	 * 
-	 * 
-	 * @return a random connection
+	 * @return a random connection regardless of levels back.
 	 */
 	public Connection getRandomConnection() {
 		// choose output or node
@@ -254,27 +352,54 @@ public class Chromosome implements Comparable<Chromosome> {
 	}
 
 	/**
-	 * This method computes a list of active connections (if necessary) and returns it.
+	 * This method computes a list of active nodes (if necessary) and returns it.
 	 *
-	 * @return
+	 * @return the list of active nodes.
 	 */
 	public ArrayList<Node> getActiveNodes() {
 		computeActiveNodes();
 		return activeNodes;
 	}
 
+	/**
+	 * For internal use only, this method actually computes the list of active nodes
+	 * from the chromosome. This is done recursively by calling {@code getActive()}
+	 * on the nodes until the first node returns.
+	 */
 	private void computeActiveNodes() {
 		// lazy recomputation has been triggered, do it
 		if (recomputeActiveNodes) {
 			recomputeActiveNodes = false;
 			activeNodes = new ArrayList<Node>();
-
+			// recursive operation
 			for (Output output : outputs) {
 				output.getActiveNodes(activeNodes);
 			}
 		}
 	}
 
+	/**
+	 * Performs a deep comparison between this chromosome and the provided one.
+	 * This is done on a gene-by-gene basis.
+	 * 
+	 * This method returns true if and only if:
+	 * <ul>
+	 * <li>the chromosomes being compared are not the same instance;</li>
+	 * <li>the connections of the compared chromosomes are not the same instance;</li>
+	 * <li>the grid position of the chromosome's elements are the same;</li>
+	 * </ul>
+	 * <br><br>
+	 * The relationship computed by this method is:
+	 * <ul>
+	 * <li>symmetric: a.copyOf(b) == b.copyOf(a);</li>
+	 * <li>not reflexive: a.copyOf(a) returns false;</li>
+	 * <li>not transitive: if a.copyOf(b) is true and b.copyOf(c) is true, a.copyOf(c) is
+	 *    not necessarily true since it is possible that a == c.</li>
+	 * </ul>
+	 * @param chromosome the chromosome to compare to.
+	 * @return true if it is a copy of this chromosome, but not the same chromosome.
+	 *
+	 */
 	public boolean compareGenesTo(Chromosome chromosome) {
 		for (int r = 0; r < resources.rows(); r++) {
 			for (int c = 0; c < resources.columns(); c++) {
@@ -293,6 +418,13 @@ public class Chromosome implements Comparable<Chromosome> {
 		return true;
 	}
 
+	/**
+	 * Does the same as {@code compareGenesto()} but only looks
+	 * at the active portion of the chromosome.
+	 * 
+	 * @param chromosome the chromosome to compare to.
+	 * @return true if the two active portions are identical.
+	 */
 	public boolean compareActiveGenesTo(Chromosome chromosome) {
 		// update list if it is out of date
 		computeActiveNodes();
@@ -308,6 +440,11 @@ public class Chromosome implements Comparable<Chromosome> {
 		return false;
 	}
 	
+	/**
+	 * Iterates through the nodes and prints all connections and functions.
+	 * This is intended for debugging purposes only and does not print to the
+	 * GUI console.
+	 */
 	public void printNodes() {
 		int arity = resources.arity();
 		
@@ -330,6 +467,9 @@ public class Chromosome implements Comparable<Chromosome> {
 		System.out.println();
 	}
 	
+	/**
+	 * @return a reference to the resources based on which the chromosome was built.
+	 */
 	public Resources getResources() {
 		return resources;
 	}
diff --git a/src/jcgp/backend/population/Connection.java b/src/jcgp/backend/population/Connection.java
index cd0f5ce..f0be590 100644
--- a/src/jcgp/backend/population/Connection.java
+++ b/src/jcgp/backend/population/Connection.java
@@ -1,7 +1,30 @@
 package jcgp.backend.population;
 
+/**
+ * {@code Connection} declares the expected behaviour of any
+ * part of a chromosome that can be connected to, specifically
+ * nodes or inputs. Outputs are not connections since they 
+ * mark the end of chromosome paths.
+ * <br><br>
+ * This interface provides a way to deal with connections
+ * generically without having to specify whether they are nodes
+ * or inputs. In this way a random connection can be picked and
+ * dealt with more easily, facilitating mutations.
+ * 
+ * @author Eduardo Pedroni
+ *
+ */
 public interface Connection {
 
+	/**
+	 * Compute and return the value of this connection. In
+	 * the case of inputs no computation is necessary, this
+	 * simply returns the value the input is set to. In the
+	 * case of nodes, the value is computed based on the
+	 * node's function and the value of its own connections.
+	 * 
+	 * @return the connection's value.
+	 */
 	public Object getValue();
 
 }
diff --git a/src/jcgp/backend/population/Gene.java b/src/jcgp/backend/population/Gene.java
deleted file mode 100644
index 6b90949..0000000
--- a/src/jcgp/backend/population/Gene.java
+++ /dev/null
@@ -1,5 +0,0 @@
-package jcgp.backend.population;
-
-public abstract class Gene {
-
-}
diff --git a/src/jcgp/backend/population/Input.java b/src/jcgp/backend/population/Input.java
index 2661f8c..97fe82b 100644
--- a/src/jcgp/backend/population/Input.java
+++ b/src/jcgp/backend/population/Input.java
@@ -1,27 +1,51 @@
 package jcgp.backend.population;
 
-public class Input extends Gene implements Connection {
+/**
+ * This is a chromosome input. Inputs are a special
+ * type of connection which simply return a set value.
+ * They do not have connections and instead provide a
+ * starting point for the chromosome's active paths.
+ * 
+ * @author Eduardo Pedroni
+ *
+ */
+public class Input implements Connection {
 	
-	private Object value = 0;
+	private Object value;
 	private int index;
 	
+	/**
+	 * Initialises a new input with the current index.
+	 * 
+	 * @param index the index of the new input.
+	 */
 	public Input(int index) {
 		this.index = index;
 	}
 	
+	/**
+	 * Sets this input's value. The new value
+	 * will now be returned by this input's
+	 * {@code getValue()} method.
+	 * 
+	 * @param newValue the value to set.
+	 */
 	public void setValue(Object newValue) {
 		value = newValue;
 	}
 
+	/**
+	 * @return the input's index.
+	 */
+	public int getIndex() {
+		return index;
+	}
+	
 	@Override
 	public Object getValue() {
 		return value;
 	}
 
-	public int getIndex() {
-		return index;
-	}
-
 	@Override
 	public String toString() {
 		return "Input " + index;
diff --git a/src/jcgp/backend/population/MutableElement.java b/src/jcgp/backend/population/MutableElement.java
index 8548e63..33f3890 100644
--- a/src/jcgp/backend/population/MutableElement.java
+++ b/src/jcgp/backend/population/MutableElement.java
@@ -1,5 +1,19 @@
 package jcgp.backend.population;
 
+/**
+ * {@code MutableElement} declares the expected behaviour of any
+ * part of a chromosome that is mutable, more specifically
+ * nodes or outputs. Inputs are not mutable since they don't have
+ * connections or functions.
+ * <br><br>
+ * This interface provides a way to deal with mutable elements
+ * generically without having to specify whether they are nodes
+ * or outputs. In this way a random mutable element can be picked and
+ * dealt with more easily, facilitating mutations.
+ * 
+ * @author Eduardo Pedroni
+ *
+ */
 public interface MutableElement {
 	
 	/**
@@ -7,28 +21,33 @@ public interface MutableElement {
 	 * Implementing classes may choose to ignore the given index (such as in the
 	 * case of outputs, which only have one connection).
 	 * 
-	 * @param index
-	 * @param newConnection
+	 * @param index the connection index to set.
+	 * @param newConnection the chromosome element to connect to.
 	 */
 	public void setConnection(int index, Connection newConnection);
 	
 	/**
-	 * This method returns true if and only if:</br>
-	 * 	- the elements being compared are not the same instance;</br>
-	 *  - the connections of the compared elements are not the same instance;</br>
-	 *  - the elements have the same function (in the case of Node);</br>
-	 *  - the grid position of the elements themselves are the same;</br>
-	 *  - the grid position of all equivalent connections are the same;</br></br>
+	 * Asserts if the specified element is a copy of the elements
+	 * this is called on.<br>
+	 * This method returns true if and only if:
+	 * <ul>
+	 * <li>the elements being compared are not the same instance;</li>
+	 * <li>the connections of the compared elements are not the same instance;</li>
+	 * <li>the elements have the same function (in the case of Node);</li>
+	 * <li>the grid position of the elements themselves are the same;</li>
+	 * <li>the grid position of all equivalent connections are the same;</li>
+	 * </ul>
+	 * <br><br>
+	 * The relationship computed by this method is:
+	 * <ul>
+	 * <li>symmetric: a.copyOf(b) == b.copyOf(a);</li>
+	 * <li>not reflexive: a.copyOf(a) returns false;</li>
+	 * <li>not transitive: if a.copyOf(b) is true and b.copyOf(c) is true, a.copyOf(c) is
+	 *    not necessarily true since it is possible that a == c.</li>
 	 * 
-	 * The relationship computed by this method is:</br>
-	 *  - symmetric: a.copyOf(b) == b.copyOf(a);</br>
-	 *  - not reflexive: a.copyOf(a) returns false;</br>
-	 *  - not transitive: if a.copyOf(b) is true and b.copyOf(c) is true, a.copyOf(c) is
-	 *    not necessarily true since it is possible that a == c.</br>
-	 * 
-	 * @param m
-	 * @return
+	 * @param element the mutable element to compare to.
+	 * @return true if {@code element} is a copy of this element.
 	 */
-	boolean copyOf(MutableElement m);
+	boolean copyOf(MutableElement element);
 	
 }
diff --git a/src/jcgp/backend/population/Node.java b/src/jcgp/backend/population/Node.java
index 74f6b54..704b24e 100644
--- a/src/jcgp/backend/population/Node.java
+++ b/src/jcgp/backend/population/Node.java
@@ -4,41 +4,63 @@ import java.util.ArrayList;
 
 import jcgp.backend.function.Function;
 
-
-public class Node extends Gene implements MutableElement, Connection {
+/**
+ * Nodes make up the main part of the chromosome, 
+ * where the actual functions are evolved. Each node
+ * contains a function and a number of connections.
+ * The node outputs the result of performing its function
+ * on the values of its connections. Nodes therefore
+ * implement both {@code MutableElement} and {@code Connection}
+ * since they can be mutated but also connected to. 
+ * Nodes are constructed with a fixed number of connections
+ * (determined by the maximum arity of the function set)
+ * and must be reinstantiated if the experiment arity
+ * changes.
+ * 
+ * @author Eduardo Pedroni
+ *
+ */
+public class Node implements MutableElement, Connection {
 
 	private Function function;
 	private Connection[] connections;
 	private int column, row;
 	private Chromosome chromosome;
 
+	/**
+	 * Constructs a new instance of {@code Node} with the
+	 * specified parameters. Nodes must contain their
+	 * own row and column for ease of copying.
+	 * 
+	 * @param chromosome the chromosome this node belongs to.
+	 * @param row the node's row.
+	 * @param column the node's column.
+	 * @param arity the maximum arity of the experiment.
+	 */
 	public Node(Chromosome chromosome, int row, int column, int arity) {
 		this.chromosome = chromosome;
 		this.column = column;
 		this.row = row;
 	}
-
-	@Override
-	public Object getValue() {
-		Object[] args = new Object[function.getArity()];
-		for (int i = 0; i < function.getArity(); i++) {
-			args[i] = connections[i].getValue();
-		}
-		return function.run(args);
-	}
-
+	
+	/**
+	 * Sets the node function.
+	 * 
+	 * @param newFunction the new function to set.
+	 */
 	public void setFunction(Function newFunction) {
 		function = newFunction;
 	}
 
-	@Override
-	public void setConnection(int index, Connection newConnection) {
-		if (newConnection != null) {
-			connections[index] = newConnection;
-			chromosome.recomputeActiveNodes();
-		}
-	}
-
+	/**
+	 * Initialises the node with the specified values.
+	 * The number of connections passed as argument must
+	 * be exactly the same as the experiment arity, or 
+	 * an {@code IllegalArgumentException} will be thrown.
+	 * 
+	 * @param newFunction the node function to set.
+	 * @param newConnections the node connections to set.
+	 */
 	public void initialise(Function newFunction, Connection ... newConnections) {
 		function = newFunction;
 		if (newConnections.length == chromosome.getResources().arity()) {
@@ -48,41 +70,71 @@ public class Node extends Gene implements MutableElement, Connection {
 		}
 	}
 
+	/**
+	 * @return this node's column.
+	 */
 	public int getColumn() {
 		return column;
 	}
 
+	/**
+	 * @return this node's row.
+	 */
 	public int getRow() {
 		return row;
 	}
 
+	/**
+	 * @return this node's function.
+	 */
 	public Function getFunction() {
 		return function;
 	}
 	
+	/**
+	 * @param index the connection to return.
+	 * @return the indexed connection.
+	 */
 	public Connection getConnection(int index) {
 		return connections[index];
 	}
 
-	public void getActive(ArrayList<Node> activeNodes) {
+	/**
+	 * For package use, this is a recursive method
+	 * used to create a collection of active nodes
+	 * in the chromosome. If this node is not already
+	 * in the active node list, this method adds it.
+	 * It then calls {@code getActive()} on each of its
+	 * connections, therefore recursively adding every
+	 * single active node to the given list.
+	 * 
+	 * @param activeNodes the list of active nodes being built.
+	 */
+	protected void getActive(ArrayList<Node> activeNodes) {
+		// don't add the same node twice
 		if (!activeNodes.contains(this)) {
 			activeNodes.add(this);
 		}
+		// call getActive on all connections - they are all active
 		for (int i = 0; i < function.getArity(); i++) {
 			if (connections[i] instanceof Node) {
 				((Node) connections[i]).getActive(activeNodes);
 			}
-
 		}
 	}
 
 	@Override
-	public boolean copyOf(MutableElement m) {
-		if (this != m) {
-			if (m instanceof Node) {
-				Node n = (Node) m;
+	public boolean copyOf(MutableElement element) {
+		// both cannot be the same instance
+		if (this != element) {
+			// element must be instance of node
+			if (element instanceof Node) {
+				Node n = (Node) element;
+				// must have the same function
 				if (function == n.getFunction()) {
+					// row and column must be the same
 					if (column == n.getColumn() && row == n.getRow()) {
+						// connections must be the equivalent, but not the same instance
 						for (int i = 0; i < connections.length; i++) {
 							if (connections[i] != n.getConnection(i)) {
 								if (connections[i] instanceof Input && n.getConnection(i) instanceof Input) {
@@ -101,6 +153,7 @@ public class Node extends Gene implements MutableElement, Connection {
 								return false;
 							}
 						}
+						// all connections checked, this really is a copy
 						return true;
 					}
 				}
@@ -108,6 +161,26 @@ public class Node extends Gene implements MutableElement, Connection {
 		}
 		return false;
 	}
+	
+	@Override
+	public Object getValue() {
+		// build list of arguments recursively
+		Object[] args = new Object[function.getArity()];
+		for (int i = 0; i < function.getArity(); i++) {
+			args[i] = connections[i].getValue();
+		}
+		// return function result
+		return function.run(args);
+	}
+
+	@Override
+	public void setConnection(int index, Connection newConnection) {
+		// connection must not be null
+		if (newConnection != null) {
+			connections[index] = newConnection;
+			chromosome.recomputeActiveNodes();
+		}
+	}
 
 	@Override
 	public String toString() {
diff --git a/src/jcgp/backend/population/Output.java b/src/jcgp/backend/population/Output.java
index d876951..ab693e2 100644
--- a/src/jcgp/backend/population/Output.java
+++ b/src/jcgp/backend/population/Output.java
@@ -2,26 +2,39 @@ package jcgp.backend.population;
 
 import java.util.ArrayList;
 
-public class Output extends Gene implements MutableElement {
+/**
+ * This is a chromosome output. Outputs are a special
+ * type of mutable element with a single connection. It
+ * returns the value of its single connection, but it 
+ * may not be connected to - it terminates a chromosome
+ * active connection path. 
+ * 
+ * @author Eduardo Pedroni
+ *
+ */
+public class Output implements MutableElement {
 	
 	private Connection source;
 	private Chromosome chromosome;
 	private int index;
 	
+	/**
+	 * Makes a new instance of {@code Output} with the 
+	 * specified arguments.
+	 * 
+	 * @param chromosome the chromosome this output belongs to.
+	 * @param index the output index.
+	 */
 	public Output(Chromosome chromosome, int index) {
 		this.chromosome = chromosome;
 		this.index = index;
 	}
 
+	/**
+	 * @return the value of the output's source.
+	 */
 	public Object calculate() {
-		Object result = source.getValue();
-		return result;
-	}
-
-	@Override
-	public void setConnection(int index, Connection newConnection) {
-		source = newConnection;
-		chromosome.recomputeActiveNodes();
+		return source.getValue();
 	}
 
 	public int getIndex() {
@@ -38,12 +51,30 @@ public class Output extends Gene implements MutableElement {
 		}
 	}
 	
+	/**
+	 * When mutating an output, the index parameter
+	 * is simply ignored and the output source is
+	 * set. 
+	 * 
+	 * @see jcgp.backend.population.MutableElement#setConnection(int, jcgp.backend.population.Connection)
+	 */
+	@Override
+	public void setConnection(int index, Connection newConnection) {
+		source = newConnection;
+		// trigger active path recomputation
+		chromosome.recomputeActiveNodes();
+	}
+	
 	@Override
 	public boolean copyOf(MutableElement m) {
+		// both cannot be the same instance
 		if (this != m) {
+			// element must be instance of output
 			if (m instanceof Output) {
 				Output o = (Output) m;
+				// index must be the same
 				if (index == o.getIndex()) {
+					// source must be the same
 					if (source != o.getSource()) {
 						if (source instanceof Input && o.getSource() instanceof Input) {
 							if (((Input) source).getIndex() == ((Input) o.getSource()).getIndex()) {
diff --git a/src/jcgp/backend/population/Population.java b/src/jcgp/backend/population/Population.java
index d99c64e..5d549e9 100644
--- a/src/jcgp/backend/population/Population.java
+++ b/src/jcgp/backend/population/Population.java
@@ -5,6 +5,35 @@ import java.util.Collections;
 
 import jcgp.backend.resources.Resources;
 
+/**
+ * This class primarily holds a collection of chromosomes. In addition, 
+ * it provides a few utility methods for manipulating and copying 
+ * chromosomes, useful for evolutionary strategies.
+ * <br><br>
+ * {@code copyChromosome()} is used to create copies of chromosomes, 
+ * though it is also possible to create a new instance of population
+ * directly from a seed chromosome using the right constructor. 
+ * <br><br>
+ * For convenience, a random chromosome can be retrieved using
+ * {@code getRandomChromosome()}, which is guaranteed to use the
+ * experiment's specified seed. If an entirely random population
+ * is needed, {@code reinitialise()} should be used to randomise
+ * all chromosomes without creating a new instance of {@code Population}.
+ * <br><br>
+ * By convention the population's chromosomes should always be sorted in
+ * order of fitness, from worst to best. This cannot be done automatically
+ * since a higher fitness value does not necessarily mean better fitness;
+ * some problem types might instead interpret fitness 0 as a perfect solution.
+ * Sorting the population is done easily using {@code sortAscending()} and
+ * {@code sortDescending()}, and should be done by the problem type as appropriate.
+ * It is <b>critical</b> to sort the population after it is evaluated as 
+ * evolutionary strategies will obey the convention and assume the population
+ * is sorted in order of fitness.
+ * 
+ * 
+ * @author Eduardo Pedroni
+ *
+ */
 public class Population {
 	
 	private final Chromosome[] chromosomes;
@@ -14,7 +43,7 @@ public class Population {
 	 * Initialise a random population according to the parameters specified
 	 * in the resources.
 	 * 
-	 * @param resources the CGP resources
+	 * @param resources the experiment's resources.
 	 */
 	public Population(Resources resources) {
 		this.resources = resources;
@@ -28,8 +57,8 @@ public class Population {
 	/**
 	 * Initialise a population of copies of the given chromosome.
 	 * 
-	 * @param parent
-	 * @param resources
+	 * @param parent the chromosome to use as a model.
+	 * @param resources a reference to the experiment's resources.
 	 */
 	public Population(Chromosome parent, Resources resources) {
 		this.resources = resources;
@@ -43,8 +72,8 @@ public class Population {
 	/**
 	 * Returns the indexed chromosome.
 	 * 
-	 * @param index
-	 * @return
+	 * @param index the chromosome to return.
+	 * @return the indexed chromosome.
 	 */
 	public Chromosome getChromosome(int index) {
 		return chromosomes[index];
@@ -65,8 +94,8 @@ public class Population {
 	 * 
 	 * This method does nothing if source == target.
 	 * 
-	 * @param source 
-	 * @param target
+	 * @param source the chromosome to copy from.
+	 * @param target the chromosome to copy to.
 	 */
 	public void copyChromosome(int source, int target) {
 		if (source != target) {
@@ -74,16 +103,40 @@ public class Population {
 		}
 	}
 	
+	/**
+	 * Loop through all chromosomes and randomise all connections
+	 * and functions.
+	 */
 	public void reinitialise() {
 		for (int c = 0; c < chromosomes.length; c++) {
 			chromosomes[c].reinitialiseConnections();
 		}
 	}
 	
+	/**
+	 * The fittest chromosome, by convention, is the last one
+	 * in the array. Problem evaluation methods are expected to
+	 * sort the population into ascending order of fitness, such
+	 * that the best chromosome is in the last position (size - 1).
+	 * This method assumes that the population is sorted as such
+	 * and returns the last chromosome in the array.
+	 * 
+	 * @return the fittest chromosome in the population.
+	 */
 	public Chromosome getFittest() {
 		return chromosomes[chromosomes.length - 1];
 	}
 	
+	/**
+	 * The fittest chromosome, by convention, is the last one
+	 * in the array. Problem evaluation methods are expected to
+	 * sort the population into ascending order of fitness, such
+	 * that the best chromosome is in the last position (size - 1).
+	 * This method assumes that the population is sorted as such
+	 * and returns the last index in the array.
+	 * 
+	 * @return the index of the fittest chromosome.
+	 */
 	public int getFittestIndex() {
 		return chromosomes.length - 1;
 	}
diff --git a/src/jcgp/backend/resources/Resources.java b/src/jcgp/backend/resources/Resources.java
index 1f79627..ebd6fb2 100644
--- a/src/jcgp/backend/resources/Resources.java
+++ b/src/jcgp/backend/resources/Resources.java
@@ -8,21 +8,22 @@ import jcgp.backend.parameters.IntegerParameter;
 
 /**
  * 
- * The resources class encapsulates all of the resources based on which the program operates.
- * Each instance of JCGP contains a single instance of Resources, which gets passed to the selected
- * modules as the program executes.
+ * Encapsulates all of the resources based on which the program operates.
+ * Each instance of JCGP contains a single instance of {@code Resources}.
  * <br><br>
  * The experiment's {@code Resources} object is passed to modules as the program operates, and
  * the actual parameter values can be obtained using getter methods. Note that, for code brevity,
  * this class's getters do not start with the word "get". For instance, to get the number of rows, 
- * one would use {@code rows()} instead of {@code getRows()}.
+ * one would use {@code rows()} instead of {@code getRows()} which doesn't exist.
  * <br><br>
  * In addition to parameters, this class also offers utility methods. Any necessary random numbers
- * should be obtained using {@code getRandomInt()} and {@code getRandomDouble()}. Functions from the
- * selected function set can be obtained through this class as well. Finally, printing to the console
- * should be done via the resources using the report and print methods, so that these prints also
- * get sent to the GUI console (if one is present).
+ * should be obtained using {@code getRandomInt()} and {@code getRandomDouble()} as these methods
+ * use a particular {@code Random} object guaranteed to generate random numbers based on the seed 
+ * parameter. Functions from the selected function set can be obtained through this class as well.
+ * Finally, printing to the console should be done via the resources using the report and print
+ * methods, so that these prints also get sent to the GUI console (if one is present).
  * 
+ * @see Parameter, Console, FunctionSet
  * @author Eduardo Pedroni
  *
  */
diff --git a/src/jcgp/backend/statistics/StatisticsLogger.java b/src/jcgp/backend/statistics/StatisticsLogger.java
index dfbcdbe..7165a11 100644
--- a/src/jcgp/backend/statistics/StatisticsLogger.java
+++ b/src/jcgp/backend/statistics/StatisticsLogger.java
@@ -173,6 +173,7 @@ public class StatisticsLogger {
 	public int getSuccessfulRuns() {
 		int count = 0;
 		for (RunEntry runEntry : runEntries) {
+			// only increment if solution was perfect
 			if (runEntry.isSuccessful()) {
 				count++;
 			}
@@ -189,7 +190,7 @@ public class StatisticsLogger {
 	 * @return the success rate across all runs.
 	 */
 	public double getSuccessRate() {
-		return getSuccessfulRuns() / runEntries.size();
+		return getSuccessfulRuns() / (double) runEntries.size();
 	}
 	
 	/**
@@ -203,6 +204,7 @@ public class StatisticsLogger {
 		double average = 0;
 		int successfulRuns = getSuccessfulRuns();
 		for (RunEntry runEntry : runEntries) {
+			// only if solution was perfect
 			if (runEntry.isSuccessful()) {
 				average += runEntry.getGeneration() / successfulRuns;
 			}
@@ -221,6 +223,7 @@ public class StatisticsLogger {
 		double average = getAverageSuccessfulGenerations();
 		double temp, stdDev = 0;
 		for (RunEntry runEntry : runEntries) {
+			// only if solution was perfect
 			if (runEntry.isSuccessful()) {
 				temp = runEntry.getGeneration() - average;
 				temp = temp * temp;