/**
* An evaluator that performs coevolutionary evaluation. Like SimpleEvaluator, it applies
* evolution pipelines, one per thread, to various subchunks of a new population.
*/
public void evaluatePopulation(final EvolutionState state) {
int numinds[] = new int[state.evalthreads];
int from[] = new int[state.evalthreads];
boolean[] assessFitness = new boolean[state.population.subpops.length];
for (int i = 0; i < assessFitness.length; i++)
assessFitness[i] = true; // update everyone's fitness in preprocess and postprocess
for (int y = 0; y < state.evalthreads; y++) {
// figure numinds
if (y < state.evalthreads - 1) // not last one
numinds[y] = state.population.subpops[0].individuals.length / state.evalthreads;
else
numinds[y] =
state.population.subpops[0].individuals.length / state.evalthreads
+ (state.population.subpops[0].individuals.length
- (state.population.subpops[0].individuals.length / state.evalthreads)
* state.evalthreads);
// figure from
from[y] = (state.population.subpops[0].individuals.length / state.evalthreads) * y;
}
randomizeOrder(state, state.population.subpops[0].individuals);
GroupedProblemForm prob = (GroupedProblemForm) (p_problem.clone());
prob.preprocessPopulation(
state, state.population, assessFitness, style == STYLE_SINGLE_ELIMINATION);
switch (style) {
case STYLE_SINGLE_ELIMINATION:
evalSingleElimination(state, state.population.subpops[0].individuals, 0, prob);
break;
case STYLE_ROUND_ROBIN:
evalRoundRobin(state, from, numinds, state.population.subpops[0].individuals, 0, prob);
break;
case STYLE_N_RANDOM_COMPETITORS_ONEWAY:
evalNRandomOneWay(state, from, numinds, state.population.subpops[0].individuals, 0, prob);
break;
case STYLE_N_RANDOM_COMPETITORS_TWOWAY:
evalNRandomTwoWay(state, from, numinds, state.population.subpops[0].individuals, 0, prob);
break;
default:
state.output.fatal(
"Invalid competition style in CompetitiveEvaluator.evaluatePopulation()");
}
prob.postprocessPopulation(
state, state.population, assessFitness, style == STYLE_SINGLE_ELIMINATION);
}
/**
* A private helper function for evalutatePopulation which evaluates a chunk of individuals in a
* subpopulation for a given thread.
*
* <p>Although this method is declared public (for the benefit of a private helper class in this
* file), you should not call it.
*
* @param state
* @param numinds
* @param from
* @param threadnum
* @param prob
*/
public void evalRoundRobinPopChunk(
final EvolutionState state,
int from,
int numinds,
int threadnum,
final Individual[] individuals,
int subpop,
final GroupedProblemForm prob) {
Individual[] competition = new Individual[2];
int[] subpops = new int[] {subpop, subpop};
boolean[] updates = new boolean[2];
updates[0] = updates[1] = true;
int upperBound = from + numinds;
// evaluate chunk of population against entire population
// since an individual x will be evaluated against all
// other individuals <x in other threads, only evaluate it against
// individuals >x in this thread.
for (int x = from; x < upperBound; x++)
for (int y = x + 1; y < individuals.length; y++) {
competition[0] = individuals[x];
competition[1] = individuals[y];
prob.evaluate(state, competition, updates, false, subpops, 0);
}
}
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 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++;
}
}
}
}
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++;
}
}
}
}
