private void enqueue(final GPNode n, final int argpos, final int depth) {
if (s_node == null) {
s_node = new GPNode[MIN_QUEUE_SIZE];
s_argpos = new int[MIN_QUEUE_SIZE];
s_depth = new int[MIN_QUEUE_SIZE];
s_size = 0;
} else if (s_size == s_node.length) // need to double them
{
GPNode[] new_s_node = new GPNode[s_size * 2];
System.arraycopy(s_node, 0, new_s_node, 0, s_size);
s_node = new_s_node;
int[] new_s_argpos = new int[s_size * 2];
System.arraycopy(s_argpos, 0, new_s_argpos, 0, s_size);
s_argpos = new_s_argpos;
int[] new_s_depth = new int[s_size * 2];
System.arraycopy(s_depth, 0, new_s_depth, 0, s_size);
s_depth = new_s_depth;
}
// okay, let's boogie!
s_node[s_size] = n;
s_argpos[s_size] = argpos;
s_depth[s_size] = depth;
s_size++;
}
public void eval(
final EvolutionState state,
final int thread,
final GPData input,
final ADFStack stack,
final GPIndividual individual,
final Problem problem) {
MultiplexerData md = (MultiplexerData) input;
long[] dat_11 = null; // quiets compiler complaints
long dat_6 = 0L;
byte dat_3 = 0;
// No shortcuts for now
children[0].eval(state, thread, input, stack, individual, problem);
if (md.status == MultiplexerData.STATUS_3) dat_3 = md.dat_3;
else if (md.status == MultiplexerData.STATUS_6) dat_6 = md.dat_6;
else // md.status == MultiplexerData.STATUS_11
{
dat_11 = md.popDat11();
System.arraycopy(md.dat_11, 0, dat_11, 0, MultiplexerData.MULTI_11_NUM_BITSTRINGS);
}
children[1].eval(state, thread, input, stack, individual, problem);
// modify
if (md.status == MultiplexerData.STATUS_3) md.dat_3 &= dat_3;
else if (md.status == MultiplexerData.STATUS_6) md.dat_6 &= dat_6;
else // md.status == MultiplexerData.STATUS_11
{
for (int x = 0; x < MultiplexerData.MULTI_11_NUM_BITSTRINGS; x++) md.dat_11[x] &= dat_11[x];
md.pushDat11(dat_11);
}
}
private static double atof(String s) {
double d = Double.valueOf(s).doubleValue();
if (Double.isNaN(d) || Double.isInfinite(d)) {
System.err.print("NaN or Infinity in input\n");
System.exit(1);
}
return (d);
}
public void setIndex(int threadnum, int index) {
if (indexes == null) indexes = new int[threadnum + 1];
if (threadnum >= indexes.length) {
int currentSize = indexes.length;
int[] temp = new int[threadnum * 2 + 1];
System.arraycopy(indexes, 0, temp, 0, currentSize);
indexes = temp;
}
indexes[threadnum] = index;
}
public void setGenomeLength(int len) {
boolean[] newGenome = new boolean[len];
System.arraycopy(
genome,
0,
newGenome,
0,
genome.length < newGenome.length ? genome.length : newGenome.length);
genome = newGenome;
}
/**
* Splits the genome into n pieces, according to points, which *must* be sorted. pieces.length
* must be 1 + points.length
*/
public void split(int[] points, Object[] pieces) {
int point0, point1;
point0 = 0;
point1 = points[0];
for (int x = 0; x < pieces.length; x++) {
pieces[x] = new boolean[point1 - point0];
System.arraycopy(genome, point0, pieces[x], 0, point1 - point0);
point0 = point1;
if (x >= pieces.length - 2) point1 = genome.length;
else point1 = points[x + 1];
}
}
/**
* Increases arguments to accommodate space if necessary. Sets adf to a. You need to then fill out
* the arguments yourself.
*/
public final void prepareADF(ADF a, GPProblem problem) {
// set to the length requested or longer
if (a.children.length > arguments.length) // the first time this will nearly always be true
{
GPData[] newarguments = new GPData[a.children.length];
System.arraycopy(arguments, 0, newarguments, 0, arguments.length);
// fill gap -- ugh, luckily this doesn't happen but a few times
for (int x = arguments.length; x < newarguments.length; x++)
newarguments[x] = (GPData) (problem.input.clone());
arguments = newarguments;
}
adf = a;
}
/** Joins the n pieces and sets the genome to their concatenation. */
public void join(Object[] pieces) {
int sum = 0;
for (int x = 0; x < pieces.length; x++) sum += ((boolean[]) (pieces[x])).length;
int runningsum = 0;
boolean[] newgenome = new boolean[sum];
for (int x = 0; x < pieces.length; x++) {
System.arraycopy(pieces[x], 0, newgenome, runningsum, ((boolean[]) (pieces[x])).length);
runningsum += ((boolean[]) (pieces[x])).length;
}
// set genome
genome = newgenome;
}
/** Sets up parameters. */
public void setup(final EvolutionState state, final Parameter base) {
if (!(state instanceof EvolutionAgent))
state.output.fatal("DRMStatistics requires an EvolutionAgent", null, null);
EvolutionAgent agent = (EvolutionAgent) state;
super.setup(state, base);
frequency = state.parameters.getIntWithDefault(base.push(P_FREQUENCY), null, 1);
store_best = state.parameters.getBoolean(base.push(P_STORE_BEST), null, true);
// use_collective = state.parameters.getBoolean(base.push(P_COLLECTIVE),null,true);
// If we are root, set up the base filename and the logtable
if (agent.iamroot) {
// I'm not sure that outputting everything to the current directory is right
basefilename =
System.getProperty("user.dir")
+ File.separator
+ state.parameters.getFile(base.push(P_STATISTICS_FILE), null).getName();
logtable = new Hashtable();
} else defaultlog = -3; // Maybe can be useful for recognizing it as a non valid log
creationtime = System.currentTimeMillis();
}
public void randomizeOrder(final EvolutionState state, final Individual[] individuals) {
// copy the inds into a new array, then dump them randomly into the
// subpopulation again
Individual[] queue = new Individual[individuals.length];
int len = queue.length;
System.arraycopy(individuals, 0, queue, 0, len);
for (int x = len; x > 0; x--) {
int i = state.random[0].nextInt(x);
individuals[x - 1] = queue[i];
// get rid of queue[i] by swapping the highest guy there and then
// decreasing the highest value :-)
queue[i] = queue[x - 1];
}
}
public void evalSingleElimination(
final EvolutionState state,
final Individual[] individuals,
final int subpop,
final GroupedProblemForm prob) {
// for a single-elimination tournament, the subpop[0] size must be 2^n for
// some value n. We don't check that here! Check it in setup.
// create the tournament array
Individual[] tourn = new Individual[individuals.length];
System.arraycopy(individuals, 0, tourn, 0, individuals.length);
int len = tourn.length;
Individual[] competition = new Individual[2];
int[] subpops = new int[] {subpop, subpop};
boolean[] updates = new boolean[2];
updates[0] = updates[1] = true;
// the "top half" of our array will be losers.
// the bottom half will be winners. Then we cut our array in half and repeat.
while (len > 1) {
for (int x = 0; x < len / 2; x++) {
competition[0] = tourn[x];
competition[1] = tourn[len - x - 1];
prob.evaluate(state, competition, updates, true, subpops, 0);
}
for (int x = 0; x < len / 2; x++) {
// if the second individual is better, or coin flip if equal, than we switch them around
if (tourn[len - x - 1].fitness.betterThan(tourn[x].fitness)
|| (tourn[len - x - 1].fitness.equivalentTo(tourn[x].fitness)
&& state.random[0].nextBoolean())) {
Individual temp = tourn[x];
tourn[x] = tourn[len - x - 1];
tourn[len - x - 1] = temp;
}
}
// last part of the tournament: deal with odd values of len!
if (len % 2 != 0) len = 1 + len / 2;
else len /= 2;
}
}
public void eval(
final EvolutionState state,
final int thread,
final GPData input,
final ADFStack stack,
final GPIndividual individual,
final Problem problem) {
MultiplexerData md = (MultiplexerData) input;
if (md.status == MultiplexerData.STATUS_3)
md.dat_3 = Fast.M_3[bitpos + MultiplexerData.STATUS_3];
else if (md.status == MultiplexerData.STATUS_6)
md.dat_6 = Fast.M_6[bitpos + MultiplexerData.STATUS_6];
else // md.status == MultiplexerData.STATUS_11
System.arraycopy(
Fast.M_11[bitpos + MultiplexerData.STATUS_11],
0,
md.dat_11,
0,
MultiplexerData.MULTI_11_NUM_BITSTRINGS);
}
public void evalNRandomOneWayPopChunk(
final EvolutionState state,
int from,
int numinds,
int threadnum,
final Individual[] individuals,
final int subpop,
final GroupedProblemForm prob) {
Individual[] queue = new Individual[individuals.length];
int len = queue.length;
System.arraycopy(individuals, 0, queue, 0, len);
Individual[] competition = new Individual[2];
int subpops[] = new int[] {subpop, subpop};
boolean[] updates = new boolean[2];
updates[0] = true;
updates[1] = false;
int upperBound = from + numinds;
for (int x = from; x < upperBound; x++) {
competition[0] = individuals[x];
// fill up our tournament
for (int y = 0; y < groupSize; ) {
// swap to end and remove
int index = state.random[0].nextInt(len - y);
competition[1] = queue[index];
queue[index] = queue[len - y - 1];
queue[len - y - 1] = competition[1];
// if the opponent is not the actual individual, we can
// have a competition
if (competition[1] != individuals[x]) {
prob.evaluate(state, competition, updates, false, subpops, 0);
y++;
}
}
}
}
/**
* This one checks that the stats message was received. If not, the message is sent again up to
* five times. Run time an best individual of run are logged.
*/
public void finalStatistics(final EvolutionState state, final int result) {
super.finalStatistics(state, result);
EvolutionAgent agent = (EvolutionAgent) state;
StatisticsData data =
new StatisticsData(
new Address(agent.getName()),
state.generation,
System.currentTimeMillis() - creationtime,
getBestIndividual(state),
getBestIndividual(state));
if (agent.iamroot) // Local logging
printStatistics(state, data); // Every statistic will go there
else { // DRM logging
for (int i = 0; i < 5; i++) { // Try to send final data 5 times
IRequest request = agent.fireMessage(agent.getRootAddress(), EvolutionAgent.M_STATS, data);
while (request.getStatus() == IRequest.WAITING) {
Thread.yield();
// try{Thread.sleep(1000);}
// catch(Exception e){state.output.error("Exception: " + e);}
}
if (request.getStatus() == IRequest.DONE) {
break;
} else {
state.output.error("There was an error sending final statistics.");
try {
Thread.sleep(1000 * i ^ 2);
} catch (Exception e) {
state.output.error("Exception: " + e);
}
}
}
}
}
public void evalNRandomTwoWayPopChunk(
final EvolutionState state,
int from,
int numinds,
int threadnum,
final Individual[] individuals,
final int subpop,
final GroupedProblemForm prob) {
// the number of games played for each player
EncapsulatedIndividual[] individualsOrdered = new EncapsulatedIndividual[individuals.length];
EncapsulatedIndividual[] queue = new EncapsulatedIndividual[individuals.length];
for (int i = 0; i < individuals.length; i++)
individualsOrdered[i] = new EncapsulatedIndividual(individuals[i], 0);
Individual[] competition = new Individual[2];
int[] subpops = new int[] {subpop, subpop};
boolean[] updates = new boolean[2];
updates[0] = true;
int upperBound = from + numinds;
for (int x = from; x < upperBound; x++) {
System.arraycopy(individualsOrdered, 0, queue, 0, queue.length);
competition[0] = queue[x].ind;
// if the rest of individuals is not enough to fill
// all games remaining for the current individual
// (meaning that the current individual has left a
// lot of games to play versus players with index
// greater than his own), then it should play with
// all. In the end, we should check that he finished
// all the games he needs. If he did, everything is
// ok, otherwise he should play with some other players
// with index smaller than his own, but all these games
// will count only for his fitness evaluation, and
// not for the opponents' (unless allowOverEvaluations is set to true)
// if true, it means that he has to play against all opponents with greater index
if (individuals.length - x - 1 <= groupSize - queue[x].nOpponentsMet) {
for (int y = x + 1; y < queue.length; y++) {
competition[1] = queue[y].ind;
updates[1] = (queue[y].nOpponentsMet < groupSize) || allowOverEvaluation;
prob.evaluate(state, competition, updates, false, subpops, 0);
queue[x].nOpponentsMet++;
if (updates[1]) queue[y].nOpponentsMet++;
}
} else // here he has to play against a selection of the opponents with greater index
{
// we can use the queue structure because we'll just rearrange the indexes
// but we should make sure we also rearrange the other vectors referring to the individuals
for (int y = 0; groupSize > queue[x].nOpponentsMet; y++) {
// swap to the end and remove from list
int index = state.random[0].nextInt(queue.length - x - 1 - y) + x + 1;
competition[1] = queue[index].ind;
updates[1] = (queue[index].nOpponentsMet < groupSize) || allowOverEvaluation;
prob.evaluate(state, competition, updates, false, subpops, 0);
queue[x].nOpponentsMet++;
if (updates[1]) queue[index].nOpponentsMet++;
// swap the players (such that a player will not be considered twice)
EncapsulatedIndividual temp = queue[index];
queue[index] = queue[queue.length - y - 1];
queue[queue.length - y - 1] = temp;
}
}
// if true, it means that the current player needs to play some games with other players with
// lower indexes.
// this is an unfortunate situation, since all those players have already had their groupSize
// games for the evaluation
if (queue[x].nOpponentsMet < groupSize) {
for (int y = queue[x].nOpponentsMet; y < groupSize; y++) {
// select a random opponent with smaller index (don't even care for duplicates)
int index;
if (x > 0) // if x is 0, then there are no players with smaller index, therefore pick a
// random one
index = state.random[0].nextInt(x);
else index = state.random[0].nextInt(queue.length - 1) + 1;
// use the opponent for the evaluation
competition[1] = queue[index].ind;
updates[1] = (queue[index].nOpponentsMet < groupSize) || allowOverEvaluation;
prob.evaluate(state, competition, updates, false, subpops, 0);
queue[x].nOpponentsMet++;
if (updates[1]) queue[index].nOpponentsMet++;
}
}
}
}
