public NeatAI crossOver(NeatAI second, double fitness1, double fitness2, double disableRate) {
NeatAI first;
// Line up links/genes
ArrayList<Integer> ai1linkListPos;
ArrayList<Integer> ai2linkListPos;
ArrayList<ArrayList<Integer>> temp = lineUpAILinks(second);
ArrayList<Integer> linkIDs = temp.get(0);
// Make it so first is the fitter individual.
if (fitness2 > fitness1) {
first = second;
second = this;
ai1linkListPos = temp.get(2);
ai2linkListPos = temp.get(1);
} else {
first = this;
ai1linkListPos = temp.get(1);
ai2linkListPos = temp.get(2);
}
// Clone data.
// System.out.println("p2");
ArrayList<Gene> firstL = first.getCloneLinks();
ArrayList<Gene> secondL = second.getCloneLinks();
ArrayList<Neuron> newN = new ArrayList<Neuron>();
ArrayList<Gene> newL = new ArrayList<Gene>();
newN = first.getCloneNeurons();
// System.out.println("p3");
for (int i = 0; i < firstL.size(); i++) {
Gene gene1 = firstL.get(i);
Gene gene2;
int gene2pos = ai2linkListPos.get(linkIDs.indexOf(gene1.getHistID()));
if (gene2pos == -1) {
gene2 = null;
} else {
gene2 = secondL.get(gene2pos);
}
if (gene2 != null && rand.nextBoolean() && !gene2.isDisabled()) {
newL.add(gene2);
} else {
newL.add(gene1);
}
}
// System.out.println("New AI");
// (SimpleMap sm_, ArrayList<Neuron> n, ArrayList<Link> l, int numInputs_, int numOutputs_) {
// System.out.println("p4");
NeatAI newai = new NeatAI(sm, newN, newL, numInputs, numOutputs);
return newai;
}
/*
* This method lines up two AI's links, aka genes, and returns the order of the lined up genes, and where they occur.
* This method returns an arraylist of arraylist of Integer.
* The outer arraylist contains arraylists on:
* 0: neuron historical ID of links present in the two AI
* 1: where in the first ai's link list the link occurs
* 2: where in the second ai's link list the link occurs
*/
public ArrayList<ArrayList<Integer>> lineUpAILinks(NeatAI second) {
ArrayList<Gene> secondL = second.getCloneLinks();
ArrayList<Integer> ai1histIDlist = new ArrayList<Integer>();
ArrayList<Integer> ai2histIDlist = new ArrayList<Integer>();
ArrayList<Integer> linkID = new ArrayList<Integer>();
ArrayList<Integer> linkList1Pos = new ArrayList<Integer>();
ArrayList<Integer> linkList2Pos = new ArrayList<Integer>();
// Get genes.
int histID;
for (Gene l : links) {
histID = l.getHistID();
linkID.add(histID);
ai1histIDlist.add(histID);
}
int pos;
for (Gene l : secondL) {
histID = l.getHistID();
pos = linkID.indexOf(histID);
if (pos == -1) {
// New histID found
linkID.add(histID);
}
ai2histIDlist.add(histID);
}
// Sort genes
// Bubble sort, meh
int lowest;
for (int i = 0; i < linkID.size(); i++) {
lowest = i;
for (int j = i + 1; j < linkID.size(); j++) {
if (linkID.get(j) < linkID.get(lowest)) {
lowest = j;
}
}
int temp = linkID.get(i);
linkID.set(i, linkID.get(lowest));
linkID.set(lowest, temp);
}
// Add gene positions
for (Integer tempID : linkID) {
linkList1Pos.add(ai1histIDlist.indexOf(tempID));
linkList2Pos.add(ai2histIDlist.indexOf(tempID));
}
ArrayList<ArrayList<Integer>> temp = new ArrayList<ArrayList<Integer>>();
temp.add(linkID);
temp.add(linkList1Pos);
temp.add(linkList2Pos);
return temp;
}
public double compatibilityDistance(NeatAI second, double c1, double c2, double c3, int minSize) {
double compatibilityDistance = 0.0;
ArrayList<Gene> secondL = second.getCloneLinks();
// Line up links/genes
ArrayList<ArrayList<Integer>> temp;
temp = lineUpAILinks(second);
ArrayList<Integer> ai1linkListPos = temp.get(1);
ArrayList<Integer> ai2linkListPos = temp.get(2);
ArrayList<Double> nodeWeightDifferences = new ArrayList<Double>();
int disjointGeneCount = 0;
int excessGeneCount = 0;
int pos1;
int pos2;
for (int i = 0; i < ai1linkListPos.size(); i++) {
pos1 = ai1linkListPos.get(i);
pos2 = ai2linkListPos.get(i);
if (pos1 == -1 || pos2 == -1) {
excessGeneCount++;
} else {
disjointGeneCount += excessGeneCount;
excessGeneCount = 0;
nodeWeightDifferences.add(
Math.abs(links.get(pos1).getWeight() - secondL.get(pos2).getWeight()));
}
}
double sum = 0;
double averageWeightDifference;
for (double weightDifference : nodeWeightDifferences) {
sum += weightDifference;
}
averageWeightDifference = sum / nodeWeightDifferences.size();
int largerGenomeSize = Math.max(links.size(), secondL.size());
if (largerGenomeSize < minSize) {
largerGenomeSize = 1;
}
compatibilityDistance =
c1 * excessGeneCount / largerGenomeSize
+ c2 * disjointGeneCount / largerGenomeSize
+ c3 * averageWeightDifference;
return compatibilityDistance;
}
