public int produce(
final int min,
final int max,
final int start,
final int subpopulation,
final Individual[] inds,
final EvolutionState state,
final int thread) {
// how many individuals should we make?
int n = typicalIndsProduced();
if (n < min) n = min;
if (n > max) n = max;
// should we bother?
if (!state.random[thread].nextBoolean(likelihood))
return reproduce(
n,
start,
subpopulation,
inds,
state,
thread,
true); // DO produce children from source -- we've not done so already
// random select one tree from treelib
// int treeIndex = (new Random()).nextInt(state.maxTreelibSize);
// GPNode TR = state.treelib[treeIndex];
// create tree: 1 - TR
GPNode OneSubTR = (GPNode) (((GPNode[]) fs.nodesByName.get("-"))[0]).lightClone();
OneSubTR.children = new GPNode[2];
RegERC oneNode = new RegERC();
oneNode.constraints = 6;
oneNode.children = new GPNode[0];
oneNode.value = 1;
OneSubTR.children[0] = oneNode;
GPInitializer initializer = ((GPInitializer) state.initializer);
for (int q = start; q < n + start; /* no increment */ ) // keep on going until we're filled up
{
// grab two individuals from our sources
if (sources[0] == sources[1]) // grab from the same source
sources[0].produce(2, 2, 0, subpopulation, parents, state, thread);
else // grab from different sources
{
sources[0].produce(1, 1, 0, subpopulation, parents, state, thread);
sources[1].produce(1, 1, 1, subpopulation, parents, state, thread);
}
// at this point, parents[] contains our two selected individuals
// random select one tree from treelib
int treeIndex = (new Random()).nextInt(state.maxTreelibSize);
GPNode TR = state.treelib[treeIndex];
OneSubTR.children[1] = (GPNode) TR.clone();
double[] randomSemanticTraining = state.semanticTreelibTraining[treeIndex];
double[] randomSemanticTesting = state.semanticTreelibTesting[treeIndex];
// are our tree values valid?
if (tree1 != TREE_UNFIXED && (tree1 < 0 || tree1 >= parents[0].trees.length))
// uh oh
state.output.fatal(
"GP Crossover Pipeline attempted to fix tree.0 to a value which was out of bounds of the array of the individual's trees. Check the pipeline's fixed tree values -- they may be negative or greater than the number of trees in an individual");
if (tree2 != TREE_UNFIXED && (tree2 < 0 || tree2 >= parents[1].trees.length))
// uh oh
state.output.fatal(
"GP Crossover Pipeline attempted to fix tree.1 to a value which was out of bounds of the array of the individual's trees. Check the pipeline's fixed tree values -- they may be negative or greater than the number of trees in an individual");
int t1 = 0;
int t2 = 0;
if (tree1 == TREE_UNFIXED || tree2 == TREE_UNFIXED) {
do
// pick random trees -- their GPTreeConstraints must be the same
{
if (tree1 == TREE_UNFIXED)
if (parents[0].trees.length > 1)
t1 = state.random[thread].nextInt(parents[0].trees.length);
else t1 = 0;
else t1 = tree1;
if (tree2 == TREE_UNFIXED)
if (parents[1].trees.length > 1)
t2 = state.random[thread].nextInt(parents[1].trees.length);
else t2 = 0;
else t2 = tree2;
} while (parents[0].trees[t1].constraints(initializer)
!= parents[1].trees[t2].constraints(initializer));
} else {
t1 = tree1;
t2 = tree2;
// make sure the constraints are okay
if (parents[0].trees[t1].constraints(initializer)
!= parents[1].trees[t2].constraints(initializer)) // uh oh
state.output.fatal(
"GP Crossover Pipeline's two tree choices are both specified by the user -- but their GPTreeConstraints are not the same");
}
// number of crossover
state.numOfSGX[state.generation][0] += 1;
GPIndividual child1 = (GPIndividual) (parents[0].lightClone());
child1.trees[0].semanticTraining = new double[randomSemanticTraining.length];
child1.trees[0].semanticTesting = new double[randomSemanticTesting.length];
GPIndividual child2 = null;
if (n - (q - start) >= 2 && !tossSecondParent) {
child2 = (GPIndividual) (parents[1].lightClone());
child2.trees[0].semanticTraining = new double[randomSemanticTraining.length];
child2.trees[0].semanticTesting = new double[randomSemanticTesting.length];
}
// geometric semantic crossover on training set
for (int i = 0; i < parents[0].trees[0].semanticTraining.length; i++) {
child1.trees[0].semanticTraining[i] =
randomSemanticTraining[i] * parents[0].trees[0].semanticTraining[i]
+ (1 - randomSemanticTraining[i]) * parents[1].trees[0].semanticTraining[i];
child1.evaluated = false;
if (child2 != null) {
child2.trees[0].semanticTraining[i] =
(1 - randomSemanticTraining[i]) * parents[0].trees[0].semanticTraining[i]
+ randomSemanticTraining[i] * parents[1].trees[0].semanticTraining[i];
child2.evaluated = false;
}
}
// geometric semantic crossover on testing set
for (int i = 0; i < parents[0].trees[0].semanticTesting.length; i++) {
child1.trees[0].semanticTesting[i] =
randomSemanticTesting[i] * parents[0].trees[0].semanticTesting[i]
+ (1 - randomSemanticTesting[i]) * parents[1].trees[0].semanticTesting[i];
if (child2 != null) {
child2.trees[0].semanticTesting[i] =
(1 - randomSemanticTesting[i]) * parents[0].trees[0].semanticTesting[i]
+ randomSemanticTesting[i] * parents[1].trees[0].semanticTesting[i];
}
}
// add the individuals to the population
inds[q] = child1;
q++;
if (q < n + start && !tossSecondParent) {
inds[q] = child2;
q++;
}
}
return n;
}
