// This is a an implementation of a uniform crossover for the binary string.
// It assumes that the the genomes are all the same length.
int
BitStringGenome::UniformCrossover(const GAGenome& a, const GAGenome& b,
GAGenome* c, GAGenome* d) {
BitStringGenome& mom=(BitStringGenome &)a;
BitStringGenome& dad=(BitStringGenome &)b;
int n = 0;
if(c && d){
BitStringGenome& sis=(BitStringGenome &)*c;
BitStringGenome& bro=(BitStringGenome &)*d;
for(int i=sis.length()-1; i>=0; i--){
if(GARandomBit()){
sis.gene(i, mom.gene(i));
bro.gene(i, dad.gene(i));
}
else{
sis.gene(i, dad.gene(i));
bro.gene(i, mom.gene(i));
}
}
n = 2;
}
else {
BitStringGenome& sis = (c ? (BitStringGenome&)*c : (BitStringGenome&)*d);
for(int i=sis.length()-1; i>=0; i--)
sis.gene(i, (GARandomBit() ? mom.gene(i) : dad.gene(i)));
n = 1;
}
return n;
}
int
GA2DBinaryStringGenome::resize(int w, int h)
{
if((unsigned int)w == nx && (unsigned int)h == ny) return sz;
if(w == GAGenome::ANY_SIZE)
w = GARandomInt(minX, maxX);
else if(w < 0)
w = nx; // do nothing
else if(minX == maxX)
minX=maxX = w;
else{
if(w < STA_CAST(int, minX)) w=minX;
if(w > STA_CAST(int, maxX)) w=maxX;
}
if(h == GAGenome::ANY_SIZE)
h = GARandomInt(minY, maxY);
else if(h < 0)
h = ny; // do nothing
else if(minY == maxY)
minY=maxY = h;
else{
if(h < STA_CAST(int, minY)) h=minY;
if(h > STA_CAST(int, maxY)) h=maxY;
}
// Move the bits into the right position. If we're smaller, then shift to
// the smaller size before we do the resize (the resize method maintains bit
// integrety). If we're larger, do the move after the resize. If we're the
// same size the we don't do anything. When we're adding more bits, the new
// bits get set randomly to 0 or 1.
if(w < STA_CAST(int,nx)){
int y=GAMin(STA_CAST(int,ny),h);
for(int j=0; j<y; j++)
GABinaryString::move(j*w,j*nx,w);
}
GABinaryString::resize(w*h);
if(w > STA_CAST(int,nx)){ // adjust the existing chunks of bits
int y=GAMin(STA_CAST(int,ny),h);
for(int j=y-1; j>=0; j--){
GABinaryString::move(j*w,j*nx,nx);
for(int i=nx; i<w; i++)
bit(j*w+i, GARandomBit());
}
}
if(h > STA_CAST(int,ny)){ // change in height is always new bits
for(int i=w*ny; i<w*h; i++)
bit(i, GARandomBit());
}
nx = w; ny = h;
_evaluated = gaFalse;
return sz;
}
/* ----------------------------------------------------------------------------
3D Binary String Genome
The order for looping through indices is depth-then-height-then-width
(ie depth loops before a single height increment, height loops before a single
width increment)
---------------------------------------------------------------------------- */
void
GA3DBinaryStringGenome::UniformInitializer(GAGenome & c)
{
GA3DBinaryStringGenome &child=(GA3DBinaryStringGenome &)c;
child.resize(GAGenome::ANY_SIZE,GAGenome::ANY_SIZE,GAGenome::ANY_SIZE);
for(int i=child.width()-1; i>=0; i--)
for(int j=child.height()-1; j>=0; j--)
for(int k=child.depth()-1; k>=0; k--)
child.gene(i,j,k, GARandomBit());
}
// Resize the genome. If someone specifies ANY_RESIZE then we pick a random
// size within the behaviour limits that have been set. If limits have been
// set and someone passes us something outside the limits, we resize to the
// closest bound. If the genome is fixed size (ie min limit equals max limit)
// then we resize to the specified value and move the min/max to match it.
int
GA1DBinaryStringGenome::resize(int l)
{
if(l == STA_CAST(int, nx)) return nx;
if(l == GAGenome::ANY_SIZE)
l = GARandomInt(minX, maxX);
else if(l < 0)
return nx; // do nothing
else if(minX == maxX)
minX=maxX=l;
else{
if(l < STA_CAST(int, minX)) l=minX;
if(l > STA_CAST(int, maxX)) l=maxX;
}
GABinaryString::resize(l);
if(l > STA_CAST(int, nx))
for(int i=nx; i<l; i++)
bit(i, GARandomBit());
nx = l;
_evaluated = gaFalse;
return sz;
}
// Evolve a new generation of genomes. A steady-state GA has no 'old'
// and 'new' populations - we pick from the current population and replace its
// members with the new ones we create. We replace the worst members of the
// preceeding population. If a genome in the tmp population is worse than
// one in the main population, the genome in the main population will be
// replaced regardless of its better score.
void
GASteadyStateGA::step()
{
int i, mut, c1, c2;
GAGenome *mom, *dad; // tmp holders for selected genomes
// Generate the individuals in the temporary population from individuals in
// the main population.
for(i=0; i<tmpPop->size()-1; i+=2){ // takes care of odd population
mom = &(pop->select());
dad = &(pop->select());
stats.numsel += 2; // keep track of number of selections
c1 = c2 = 0;
if(GAFlipCoin(pCrossover())){
stats.numcro += (*scross)(*mom, *dad, &tmpPop->individual(i),
&tmpPop->individual(i+1));
c1 = c2 = 1;
}
else{
tmpPop->individual( i ).copy(*mom);
tmpPop->individual(i+1).copy(*dad);
}
stats.nummut += (mut = tmpPop->individual( i ).mutate(pMutation()));
if(mut > 0) c1 = 1;
stats.nummut += (mut = tmpPop->individual(i+1).mutate(pMutation()));
if(mut > 0) c2 = 1;
stats.numeval += c1 + c2;
}
if(tmpPop->size() % 2 != 0){ // do the remaining population member
mom = &(pop->select());
dad = &(pop->select());
stats.numsel += 2; // keep track of number of selections
c1 = 0;
if(GAFlipCoin(pCrossover())){
stats.numcro += (*scross)(*mom, *dad,
&tmpPop->individual(i), (GAGenome*)0);
c1 = 1;
}
else{
if(GARandomBit())
tmpPop->individual( i ).copy(*mom);
else
tmpPop->individual( i ).copy(*dad);
}
stats.nummut += (mut = tmpPop->individual( i ).mutate(pMutation()));
if(mut > 0) c1 = 1;
stats.numeval += c1;
}
// Replace the worst genomes in the main population with all of the individuals
// we just created. Notice that we invoke the population's add member with a
// genome pointer rather than reference. This way we don't force a clone of
// the genome - we just let the population take over. Then we take it back by
// doing a remove then a replace in the tmp population.
for(i=0; i<tmpPop->size(); i++)
pop->add(&tmpPop->individual(i));
pop->evaluate(); // get info about current pop for next time
pop->scale(); // remind the population to do its scaling
// the individuals in tmpPop are all owned by pop, but tmpPop does not know
// that. so we use replace to take the individuals from the pop and stick
// them back into tmpPop
for(i=0; i<tmpPop->size(); i++)
tmpPop->replace(pop->remove(GAPopulation::WORST, GAPopulation::SCALED), i);
stats.numrep += tmpPop->size();
stats.update(*pop); // update the statistics by one generation
}
// Evolve a new generation of genomes. A steady-state GA has no 'old'
// and 'new' populations - we pick from the current population and replace its
// members with the new ones we create. We generate either one or two children
// each 'generation'. The replacement strategy is set by the GA.
void
GAIncrementalGA::step()
{
int mut, c1, c2;
GAGenome *mom, *dad; // tmp holders for selected genomes
mom = &(pop->select());
dad = &(pop->select());
stats.numsel += 2; // keep track of the number of selections
if(noffspr == 1){
c1 = 0;
if(GAFlipCoin(pCrossover())){
stats.numcro += (*scross)(*mom, *dad, child1, (GAGenome*)0);
c1 = 1;
}
else{
if(GARandomBit())
child1->copy(*mom);
else
child1->copy(*dad);
}
stats.nummut += (mut = child1->mutate(pMutation()));
if(mut > 0) c1 = 1;
stats.numeval += c1;
if(rs == PARENT)
child1 = pop->replace(child1, mom);
else if(rs == CUSTOM)
child1 = pop->replace(child1, &(rf(*child1, *pop)));
else
child1 = pop->replace(child1, rs);
stats.numrep += 1;
}
else{
c1 = c2 = 0;
if(GAFlipCoin(pCrossover())){
stats.numcro += (*scross)(*mom, *dad, child1, child2);
c1 = c2 = 1;
}
else{
child1->copy(*mom);
child2->copy(*dad);
}
stats.nummut += (mut = child1->mutate(pMutation()));
if(mut > 0) c1 = 1;
stats.nummut += (mut = child2->mutate(pMutation()));
if(mut > 0) c2 = 1;
stats.numeval += c1 + c2;
if(rs == PARENT){
child1 = pop->replace(child1, mom);
if(mom == dad) // this is a possibility, if so do worst
child2 = pop->replace(child2, GAPopulation::WORST);
else
child2 = pop->replace(child2, dad);
}
else if(rs == CUSTOM){
child1 = pop->replace(child1, &(rf(*child1, *pop)));
child2 = pop->replace(child2, &(rf(*child2, *pop)));
}
else{
child1 = pop->replace(child1, rs);
child2 = pop->replace(child2, rs);
}
stats.numrep += 2;
}
pop->evaluate(gaTrue); // allow pop-based evaluators to do their thing
stats.update(*pop); // update the statistics for this generation
}
// The random initializer sets the bits in the bit string randomly to 0 or 1.
// It uses the GARandomBit function to do this (GARandomBit is pretty efficient
// in terms of its calls to your system's random function).
void
BitStringGenome::UniformInitializer(GAGenome & c) {
BitStringGenome &genome=(BitStringGenome &)c;
for(int i=genome.length()-1; i>=0; i--)
genome.gene(i, GARandomBit());
}
// Evolve a new generation of genomes. When we start this routine, pop
// contains the current generation. When we finish, pop contains the new
// generation and oldPop contains the (no longer) current generation. The
// previous old generation is lost. We don't deallocate any memory, we just
// reset the contents of the genomes.
// The selection routine must return a pointer to a genome from the old
// population.
void
GASimpleGA::step()
{
int i, mut, c1, c2;
GAGenome *mom, *dad; // tmp holders for selected genomes
GAPopulation *tmppop; // Swap the old population with the new pop.
tmppop = oldPop; // When we finish the ++ we want the newly
oldPop = pop; // generated population to be current (for
pop = tmppop; // references to it from member functions).
// Generate the individuals in the temporary population from individuals in
// the main population.
for(i=0; i<pop->size()-1; i+=2){ // takes care of odd population
mom = &(oldPop->select());
dad = &(oldPop->select());
stats.numsel += 2; // keep track of number of selections
c1 = c2 = 0;
if(GAFlipCoin(pCrossover())){
stats.numcro += (*scross)(*mom, *dad,
&pop->individual(i), &pop->individual(i+1));
c1 = c2 = 1;
}
else{
pop->individual( i ).copy(*mom);
pop->individual(i+1).copy(*dad);
}
stats.nummut += (mut = pop->individual( i ).mutate(pMutation()));
if(mut > 0) c1 = 1;
stats.nummut += (mut = pop->individual(i+1).mutate(pMutation()));
if(mut > 0) c2 = 1;
stats.numeval += c1 + c2;
}
if(pop->size() % 2 != 0){ // do the remaining population member
mom = &(oldPop->select());
dad = &(oldPop->select());
stats.numsel += 2; // keep track of number of selections
c1 = 0;
if(GAFlipCoin(pCrossover())){
stats.numcro += (*scross)(*mom, *dad, &pop->individual(i), (GAGenome*)0);
c1 = 1;
}
else{
if(GARandomBit())
pop->individual( i ).copy(*mom);
else
pop->individual( i ).copy(*dad);
}
stats.nummut += (mut = pop->individual( i ).mutate(pMutation()));
if(mut > 0) c1 = 1;
stats.numeval += c1;
}
// Pass mpi_tasks and mpi_rank to the population clas
pop->mpi_tasks(vmpi_tasks);
pop->mpi_rank(vmpi_rank);
stats.numrep += pop->size();
pop->evaluate(gaTrue); // get info about current pop for next time
// If we are supposed to be elitist, carry the best individual from the old
// population into the current population. Be sure to check whether we are
// supposed to minimize or maximize.
if(minimaxi() == GAGeneticAlgorithm::MAXIMIZE) {
if(el && oldPop->best().score() > pop->best().score())
oldPop->replace(pop->replace(&(oldPop->best()), GAPopulation::WORST),
GAPopulation::BEST);
}
else {
if(el && oldPop->best().score() < pop->best().score())
oldPop->replace(pop->replace(&(oldPop->best()), GAPopulation::WORST),
GAPopulation::BEST);
}
stats.update(*pop); // update the statistics by one generation
}
// Pick a single point in the 3D block and grab alternating quadrants for each
// child. If the children are resizable, this crossover does clipping.
template <class T> int
GA3DArrayGenome<T>::
OnePointCrossover(const GAGenome& p1, const GAGenome& p2,
GAGenome* c1, GAGenome* c2){
GA3DArrayGenome<T> &mom=(GA3DArrayGenome<T> &)p1;
GA3DArrayGenome<T> &dad=(GA3DArrayGenome<T> &)p2;
int nc=0;
unsigned int momsitex, momlenx, momsitey, momleny, momsitez, momlenz;
unsigned int dadsitex, dadlenx, dadsitey, dadleny, dadsitez, dadlenz;
unsigned int sitex, lenx, sitey, leny, sitez, lenz;
if(c1 && c2){
GA3DArrayGenome<T> &sis=(GA3DArrayGenome<T> &)*c1;
GA3DArrayGenome<T> &bro=(GA3DArrayGenome<T> &)*c2;
if(sis.resizeBehaviour(GAGenome::WIDTH) == GAGenome::FIXED_SIZE &&
bro.resizeBehaviour(GAGenome::WIDTH) == GAGenome::FIXED_SIZE){
if(mom.width() != dad.width() ||
sis.width() != bro.width() ||
sis.width() != mom.width()){
GAErr(GA_LOC, mom.className(), "one-point cross", gaErrSameLengthReqd);
return nc;
}
sitex = momsitex = dadsitex = GARandomInt(0, mom.width());
lenx = momlenx = dadlenx = mom.width() - momsitex;
}
else if(sis.resizeBehaviour(GAGenome::WIDTH) == GAGenome::FIXED_SIZE ||
bro.resizeBehaviour(GAGenome::WIDTH) == GAGenome::FIXED_SIZE){
GAErr(GA_LOC, mom.className(), "one-point cross", gaErrSameBehavReqd);
return nc;
}
else{
momsitex = GARandomInt(0, mom.width());
dadsitex = GARandomInt(0, dad.width());
momlenx = mom.width() - momsitex;
dadlenx = dad.width() - dadsitex;
sitex = GAMin(momsitex, dadsitex);
lenx = GAMin(momlenx, dadlenx);
}
if(sis.resizeBehaviour(GAGenome::HEIGHT) == GAGenome::FIXED_SIZE &&
bro.resizeBehaviour(GAGenome::HEIGHT) == GAGenome::FIXED_SIZE){
if(mom.height() != dad.height() ||
sis.height() != bro.height() ||
sis.height() != mom.height()){
GAErr(GA_LOC, mom.className(), "one-point cross", gaErrSameLengthReqd);
return nc;
}
sitey = momsitey = dadsitey = GARandomInt(0, mom.height());
leny = momleny = dadleny = mom.height() - momsitey;
}
else if(sis.resizeBehaviour(GAGenome::HEIGHT) == GAGenome::FIXED_SIZE ||
bro.resizeBehaviour(GAGenome::HEIGHT) == GAGenome::FIXED_SIZE){
GAErr(GA_LOC, mom.className(), "one-point cross", gaErrSameBehavReqd);
return nc;
}
else{
momsitey = GARandomInt(0, mom.height());
dadsitey = GARandomInt(0, dad.height());
momleny = mom.height() - momsitey;
dadleny = dad.height() - dadsitey;
sitey = GAMin(momsitey, dadsitey);
leny = GAMin(momleny, dadleny);
}
if(sis.resizeBehaviour(GAGenome::DEPTH) == GAGenome::FIXED_SIZE &&
bro.resizeBehaviour(GAGenome::DEPTH) == GAGenome::FIXED_SIZE){
if(mom.depth() != dad.depth() ||
sis.depth() != bro.depth() ||
sis.depth() != mom.depth()){
GAErr(GA_LOC, mom.className(), "one-point cross", gaErrSameLengthReqd);
return nc;
}
sitez = momsitez = dadsitez = GARandomInt(0, mom.depth());
lenz = momlenz = dadlenz = mom.depth() - momsitez;
}
else if(sis.resizeBehaviour(GAGenome::DEPTH) == GAGenome::FIXED_SIZE ||
bro.resizeBehaviour(GAGenome::DEPTH) == GAGenome::FIXED_SIZE){
GAErr(GA_LOC, mom.className(), "one-point cross", gaErrSameBehavReqd);
return nc;
}
else{
momsitez = GARandomInt(0, mom.depth());
dadsitez = GARandomInt(0, dad.depth());
momlenz = mom.depth() - momsitez;
dadlenz = dad.depth() - dadsitez;
sitez = GAMin(momsitez, dadsitez);
lenz = GAMin(momlenz, dadlenz);
}
sis.resize(sitex+lenx, sitey+leny, sitez+lenz);
bro.resize(sitex+lenx, sitey+leny, sitez+lenz);
sis.copy(mom,
0, 0, 0,
momsitex-sitex, momsitey-sitey, momsitez-sitez,
sitex, sitey, sitez);
sis.copy(dad,
sitex, 0, 0,
dadsitex, dadsitey-sitey, dadsitez-sitez,
lenx, sitey, sitez);
sis.copy(dad,
0, sitey, 0,
dadsitex-sitex, dadsitey, dadsitez-sitez,
sitex, leny, sitez);
sis.copy(mom,
sitex, sitey, 0,
momsitex, momsitey, momsitez-sitez,
lenx, leny, sitez);
sis.copy(dad,
0, 0, sitez,
dadsitex-sitex, dadsitey-sitey, dadsitez,
sitex, sitey, lenz);
sis.copy(mom,
sitex, 0, sitez,
momsitex, momsitey-sitey, momsitez,
lenx, sitey, lenz);
sis.copy(mom,
0, sitey, sitez,
momsitex-sitex, momsitey, momsitez,
sitex, leny, lenz);
sis.copy(dad,
sitex, sitey, sitez,
dadsitex, dadsitey, dadsitez,
lenx, leny, lenz);
bro.copy(dad,
0, 0, 0,
dadsitex-sitex, dadsitey-sitey, dadsitez-sitez,
sitex, sitey, sitez);
bro.copy(mom,
sitex, 0, 0,
momsitex, momsitey-sitey, momsitez-sitez,
lenx, sitey, sitez);
bro.copy(mom,
0, sitey, 0,
momsitex-sitex, momsitey, momsitez-sitez,
sitex, leny, sitez);
bro.copy(dad,
sitex, sitey, 0,
dadsitex, dadsitey, dadsitez-sitez,
lenx, leny, sitez);
bro.copy(mom,
0, 0, sitez,
momsitex-sitex, momsitey-sitey, momsitez,
sitex, sitey, lenz);
bro.copy(dad,
sitex, 0, sitez,
dadsitex, dadsitey-sitey, dadsitez,
lenx, sitey, lenz);
bro.copy(dad,
0, sitey, sitez,
dadsitex-sitex, dadsitey, dadsitez,
sitex, leny, lenz);
bro.copy(mom,
sitex, sitey, sitez,
momsitex, momsitey, momsitez,
lenx, leny, lenz);
nc = 2;
}
else if(c1){
GA3DArrayGenome<T> &sis=(GA3DArrayGenome<T> &)*c1;
if(sis.resizeBehaviour(GAGenome::WIDTH) == GAGenome::FIXED_SIZE){
if(mom.width() != dad.width() || sis.width() != mom.width()){
GAErr(GA_LOC, mom.className(), "one-point cross", gaErrSameLengthReqd);
return nc;
}
sitex = momsitex = dadsitex = GARandomInt(0, mom.width());
lenx = momlenx = dadlenx = mom.width() - momsitex;
}
else{
momsitex = GARandomInt(0, mom.width());
dadsitex = GARandomInt(0, dad.width());
momlenx = mom.width() - momsitex;
dadlenx = dad.width() - dadsitex;
sitex = GAMin(momsitex, dadsitex);
lenx = GAMin(momlenx, dadlenx);
}
if(sis.resizeBehaviour(GAGenome::HEIGHT) == GAGenome::FIXED_SIZE){
if(mom.height() != dad.height() || sis.height() != mom.height()){
GAErr(GA_LOC, mom.className(), "one-point cross", gaErrSameLengthReqd);
return nc;
}
sitey = momsitey = dadsitey = GARandomInt(0, mom.height());
leny = momleny = dadleny = mom.height() - momsitey;
}
else{
momsitey = GARandomInt(0, mom.height());
dadsitey = GARandomInt(0, dad.height());
momleny = mom.height() - momsitey;
dadleny = dad.height() - dadsitey;
sitey = GAMin(momsitey, dadsitey);
leny = GAMin(momleny, dadleny);
}
if(sis.resizeBehaviour(GAGenome::DEPTH) == GAGenome::FIXED_SIZE){
if(mom.depth() != dad.depth() || sis.depth() != mom.depth()){
GAErr(GA_LOC, mom.className(), "one-point cross", gaErrSameLengthReqd);
return nc;
}
sitez = momsitez = dadsitez = GARandomInt(0, mom.depth());
lenz = momlenz = dadlenz = mom.depth() - momsitez;
}
else{
momsitez = GARandomInt(0, mom.depth());
dadsitez = GARandomInt(0, dad.depth());
momlenz = mom.depth() - momsitez;
dadlenz = dad.depth() - dadsitez;
sitez = GAMin(momsitez, dadsitez);
lenz = GAMin(momlenz, dadlenz);
}
sis.resize(sitex+lenx, sitey+leny, sitez+lenz);
if(GARandomBit()){
sis.copy(mom,
0, 0, 0,
momsitex-sitex, momsitey-sitey, momsitez-sitez,
sitex, sitey, sitez);
sis.copy(dad,
sitex, 0, 0,
dadsitex, dadsitey-sitey, dadsitez-sitez,
lenx, sitey, sitez);
sis.copy(dad,
0, sitey, 0,
dadsitex-sitex, dadsitey, dadsitez-sitez,
sitex, leny, sitez);
sis.copy(mom,
sitex, sitey, 0,
momsitex, momsitey, momsitez-sitez,
lenx, leny, sitez);
sis.copy(dad,
0, 0, sitez,
dadsitex-sitex, dadsitey-sitey, dadsitez,
sitex, sitey, lenz);
sis.copy(mom,
sitex, 0, sitez,
momsitex, momsitey-sitey, momsitez,
lenx, sitey, lenz);
sis.copy(mom,
0, sitey, sitez,
momsitex-sitex, momsitey, momsitez,
sitex, leny, lenz);
sis.copy(dad,
sitex, sitey, sitez,
dadsitex, dadsitey, dadsitez,
lenx, leny, lenz);
}
else{
sis.copy(dad,
0, 0, 0,
dadsitex-sitex, dadsitey-sitey, dadsitez-sitez,
sitex, sitey, sitez);
sis.copy(mom,
sitex, 0, 0,
momsitex, momsitey-sitey, momsitez-sitez,
lenx, sitey, sitez);
sis.copy(mom,
0, sitey, 0,
momsitex-sitex, momsitey, momsitez-sitez,
sitex, leny, sitez);
sis.copy(dad,
sitex, sitey, 0,
dadsitex, dadsitey, dadsitez-sitez,
lenx, leny, sitez);
sis.copy(mom,
0, 0, sitez,
momsitex-sitex, momsitey-sitey, momsitez,
sitex, sitey, lenz);
sis.copy(dad,
sitex, 0, sitez,
dadsitex, dadsitey-sitey, dadsitez,
lenx, sitey, lenz);
sis.copy(dad,
0, sitey, sitez,
dadsitex-sitex, dadsitey, dadsitez,
sitex, leny, lenz);
sis.copy(mom,
sitex, sitey, sitez,
momsitex, momsitey, momsitez,
lenx, leny, lenz);
}
nc = 1;
}
return nc;
}
// Make sure our bitmask is big enough, generate a mask, then use it to
// extract the information from each parent to stuff the two children.
// We don't deallocate any space for the masks under the assumption that we'll
// have to use them again in the future.
// For now we'll implement this only for fixed length genomes. If you use
// this crossover method on genomes of different sizes it might break!
template <class T> int
GA3DArrayGenome<T>::
UniformCrossover(const GAGenome& p1, const GAGenome& p2,
GAGenome* c1, GAGenome* c2){
GA3DArrayGenome<T> &mom=(GA3DArrayGenome<T> &)p1;
GA3DArrayGenome<T> &dad=(GA3DArrayGenome<T> &)p2;
int nc=0;
int i,j,k;
if(c1 && c2){
GA3DArrayGenome<T> &sis=(GA3DArrayGenome<T> &)*c1;
GA3DArrayGenome<T> &bro=(GA3DArrayGenome<T> &)*c2;
if(sis.width() == bro.width() && sis.height() == bro.height() &&
sis.depth() == bro.depth() &&
mom.width() == dad.width() && mom.height() == dad.height() &&
mom.depth() == dad.depth() &&
sis.width() == mom.width() && sis.height() == mom.height() &&
sis.depth() == mom.depth()){
for(i=sis.width()-1; i>=0; i--){
for(j=sis.height()-1; j>=0; j--){
for(k=sis.depth()-1; k>=0; k--){
if(GARandomBit()){
sis.gene(i,j,k, mom.gene(i,j,k));
bro.gene(i,j,k, dad.gene(i,j,k));
}
else{
sis.gene(i,j,k, dad.gene(i,j,k));
bro.gene(i,j,k, mom.gene(i,j,k));
}
}
}
}
}
else{
GAMask mask;
int startx, starty, startz;
int maxx = (sis.width() > bro.width()) ? sis.width() : bro.width();
int minx = (mom.width() < dad.width()) ? mom.width() : dad.width();
int maxy = (sis.height() > bro.height()) ? sis.height() : bro.height();
int miny = (mom.height() < dad.height()) ? mom.height() : dad.height();
int maxz = (sis.depth() > bro.depth()) ? sis.depth() : bro.depth();
int minz = (mom.depth() < dad.depth()) ? mom.depth() : dad.depth();
mask.size(maxx*maxy*maxz);
for(i=0; i<maxx; i++)
for(j=0; j<maxy; j++)
for(k=0; k<maxz; k++)
mask[i*maxy*maxz+j*maxz+k] = GARandomBit();
startx = (sis.width() < minx) ? sis.width() : minx;
starty = (sis.height() < miny) ? sis.height() : miny;
startz = (sis.depth() < minz) ? sis.depth() : minz;
for(i=startx-1; i>=0; i--)
for(j=starty-1; j>=0; j--)
for(k=startz-1; k>=0; k--)
sis.gene(i,j,k, (mask[i*starty*startz+j*startz+k] ?
mom.gene(i,j,k) : dad.gene(i,j,k)));
startx = (bro.width() < minx) ? bro.width() : minx;
starty = (bro.height() < miny) ? bro.height() : miny;
startz = (bro.depth() < minz) ? bro.depth() : minz;
for(i=startx-1; i>=0; i--)
for(j=starty-1; j>=0; j--)
for(k=startz-1; k>=0; k--)
bro.gene(i,j,k, (mask[i*starty*startz+j*startz+k] ?
dad.gene(i,j,k) : mom.gene(i,j,k)));
}
nc = 2;
}
else if(c1){
GA3DArrayGenome<T> &sis=(GA3DArrayGenome<T> &)*c1;
if(mom.width() == dad.width() && mom.height() == dad.height() &&
mom.depth() == dad.depth() &&
sis.width() == mom.width() && sis.height() == mom.height() &&
sis.depth() == mom.depth()){
for(i=sis.width()-1; i>=0; i--)
for(j=sis.height()-1; j>=0; j--)
for(k=sis.depth()-1; k>=0; k--)
sis.gene(i,j,k, (GARandomBit() ? mom.gene(i,j,k):dad.gene(i,j,k)));
}
else{
int minx = (mom.width() < dad.width()) ? mom.width() : dad.width();
int miny = (mom.height() < dad.height()) ? mom.height() : dad.height();
int minz = (mom.depth() < dad.depth()) ? mom.depth() : dad.depth();
minx = (sis.width() < minx) ? sis.width() : minx;
miny = (sis.height() < miny) ? sis.height() : miny;
minz = (sis.depth() < minz) ? sis.depth() : minz;
for(i=minx-1; i>=0; i--)
for(j=miny-1; j>=0; j--)
for(k=minz-1; k>=0; k--)
sis.gene(i,j,k, (GARandomBit() ? mom.gene(i,j,k):dad.gene(i,j,k)));
}
nc = 1;
}
return nc;
}
int
GA3DBinaryStringGenome::resize(int w, int h, int d)
{
if(w == (int)nx && h == (int)ny && d == (int)nz) return sz;
if(w == GAGenome::ANY_SIZE)
w = GARandomInt(minX, maxX);
else if(w < 0)
w = nx; // do nothing
else if(minX == maxX)
minX=maxX = w;
else{
if(w < (int)minX) w=minX;
if(w > (int)maxX) w=maxX;
}
if(h == GAGenome::ANY_SIZE)
h = GARandomInt(minY, maxY);
else if(h < 0)
h = ny; // do nothing
else if(minY == maxY)
minY=maxY = h;
else{
if(h < (int)minY) h=minY;
if(h > (int)maxY) h=maxY;
}
if(d == GAGenome::ANY_SIZE)
d = GARandomInt(minZ, maxZ);
else if(d < 0)
d = nz; // do nothing
else if(minZ == maxZ)
minZ=maxZ = d;
else{
if(d < (int)minZ) d=minZ;
if(d > (int)maxZ) d=maxZ;
}
if(w < (int)nx && h < (int)ny){
int z=GAMin((int)nz,d);
for(int k=0; k<z; k++)
for(int j=0; j<h; j++)
GABinaryString::move(k*h*w+j*w,k*ny*nx+j*nx,w);
}
else if(w < (int)nx){
int z=GAMin((int)nz,d);
for(int k=0; k<z; k++)
for(int j=0; j<(int)ny; j++)
GABinaryString::move(k*ny*w+j*w,k*ny*nx+j*nx,w);
}
else if(h < (int)ny){
int z=GAMin((int)nz,d);
for(int k=0; k<z; k++)
for(int j=0; j<h; j++)
GABinaryString::move(k*h*nx+j*nx,k*ny*nx+j*nx,nx);
}
GABinaryString::resize(w*h*d);
if(w > (int)nx && h > (int)ny){ // adjust the existing bits
int z=GAMin((int)nz,d);
for(int k=z-1; k>=0; k--){
int j;
for(j=ny-1; j>=0; j--){
GABinaryString::move(k*h*w+j*w,k*ny*nx+j*nx,nx);
for(int i=nx; i<w; i++)
bit(k*h*w+j*w+i, GARandomBit());
}
for(j=ny; j<h; j++)
for(int i=0; i<w; i++)
bit(k*h*w+j*w+i, GARandomBit());
}
}
else if(w > (int)nx){
int z=GAMin((int)nz,d);
for(int k=z-1; k>=0; k--){
for(int j=h-1; j>=0; j--){
GABinaryString::move(k*h*w+j*w,k*h*nx+j*nx,nx);
for(int i=nx; i<w; i++)
bit(k*h*w+j*w+i, GARandomBit());
}
}
}
else if(h > (int)ny){
int z=GAMin((int)nz,d);
for(int k=z-1; k>=0; k--){
int j;
for(j=ny-1; j>=0; j--)
GABinaryString::move(k*h*w+j*w,k*ny*w+j*w,w);
for(j=ny; j<h; j++)
for(int i=0; i<w; i++)
bit(k*h*w+j*w+i, GARandomBit());
}
}
if(d > (int)nz){ // change in depth is always new bits
for(int i=w*h*nz; i<w*h*d; i++)
bit(i, GARandomBit());
}
nx = w; ny = h; nz = d;
_evaluated = gaFalse;
return sz;
}
// Make sure our bitmask is big enough, generate a mask, then use it to
// extract the information from each parent to stuff the two children.
// We don't deallocate any space for the masks under the assumption that we'll
// have to use them again in the future.
// For now we'll implement this only for fixed length genomes. If you use
// this crossover method on genomes of different sizes it might break!
int
GA3DBinaryStringGenome::
UniformCrossover(const GAGenome& p1, const GAGenome& p2,
GAGenome* c1, GAGenome* c2){
GA3DBinaryStringGenome &mom=(GA3DBinaryStringGenome &)p1;
GA3DBinaryStringGenome &dad=(GA3DBinaryStringGenome &)p2;
int i,j,k, nc=0;;
if(c1 && c2){
GA3DBinaryStringGenome &sis=(GA3DBinaryStringGenome &)*c1;
GA3DBinaryStringGenome &bro=(GA3DBinaryStringGenome &)*c2;
if(sis.width() == bro.width() && sis.height() == bro.height() &&
sis.depth() == bro.depth() &&
mom.width() == dad.width() && mom.height() == dad.height() &&
mom.depth() == dad.depth() &&
sis.width() == mom.width() && sis.height() == mom.height() &&
sis.depth() == mom.depth()){
for(i=sis.width()-1; i>=0; i--){
for(j=sis.height()-1; j>=0; j--){
for(k=sis.depth()-1; k>=0; k--){
if(GARandomBit()){
sis.gene(i,j,k, mom.gene(i,j,k));
bro.gene(i,j,k, dad.gene(i,j,k));
}
else{
sis.gene(i,j,k, dad.gene(i,j,k));
bro.gene(i,j,k, mom.gene(i,j,k));
}
}
}
}
}
else{
GAMask mask;
int maxx = GAMax(sis.width(), bro.width());
int minx = GAMin(mom.width(), dad.width());
int maxy = GAMax(sis.height(), bro.height());
int miny = GAMin(mom.height(), dad.height());
int maxz = GAMax(sis.depth(), bro.depth());
int minz = GAMin(mom.depth(), dad.depth());
mask.size(maxx*maxy*maxz);
for(i=0; i<maxx; i++)
for(j=0; j<maxy; j++)
for(k=0; k<maxz; k++)
mask[i*maxy*maxz+j*maxz+k] = GARandomBit();
minx = GAMin(sis.width(), minx);
miny = GAMin(sis.height(), miny);
minz = GAMin(sis.depth(), minz);
for(i=minx-1; i>=0; i--)
for(j=miny-1; j>=0; j--)
for(k=minz-1; k>=0; k--)
sis.gene(i,j,k, (mask[i*miny*minz+j*minz+k] ?
mom.gene(i,j,k) : dad.gene(i,j,k)));
minx = GAMin(bro.width(), minx);
miny = GAMin(bro.height(), miny);
minz = GAMin(bro.depth(), minz);
for(i=minx-1; i>=0; i--)
for(j=miny-1; j>=0; j--)
for(k=minz-1; k>=0; k--)
bro.gene(i,j,k, (mask[i*miny*minz+j*minz+k] ?
dad.gene(i,j,k) : mom.gene(i,j,k)));
}
nc = 2;
}
else if(c1 || c2){
GA3DBinaryStringGenome &sis =
(c1 ? (GA3DBinaryStringGenome&)*c1 : (GA3DBinaryStringGenome&)*c2);
if(mom.width() == dad.width() && mom.height() == dad.height() &&
mom.depth() == dad.depth() &&
sis.width() == mom.width() && sis.height() == mom.height() &&
sis.depth() == mom.depth()){
for(i=sis.width()-1; i>=0; i--)
for(j=sis.height()-1; j>=0; j--)
for(k=sis.depth()-1; k>=0; k--)
sis.gene(i,j,k, (GARandomBit() ? mom.gene(i,j,k):dad.gene(i,j,k)));
}
else{
int minx = GAMin(mom.width(), dad.width());
int miny = GAMin(mom.height(), dad.height());
int minz = GAMin(mom.depth(), dad.depth());
minx = GAMin(sis.width(), minx);
miny = GAMin(sis.height(), miny);
minz = GAMin(sis.depth(), minz);
for(i=minx-1; i>=0; i--)
for(j=miny-1; j>=0; j--)
for(k=minz-1; k>=0; k--)
sis.gene(i,j,k, (GARandomBit() ? mom.gene(i,j,k):dad.gene(i,j,k)));
}
nc = 1;
}
return nc;
}
// To evolve the genetic algorithm, we loop through all of our populations and
// evolve each one of them. Then allow the migrator to do its thing. Assumes
// that the tmp pop is at least as big as the largest nrepl that we'll use.
// The master population maintains the best n individuals from each of the
// populations, and it is based on those that we keep the statistics for the
// entire genetic algorithm run.
void
GADemeGA::step() {
int i, mut, c1, c2;
GAGenome *mom, *dad;
float pc;
if(!scross) pc = 0.0;
else pc = pCrossover();
for(unsigned int ii=0; ii<npop; ii++) {
for(i=0; i<nrepl[ii]-1; i+=2){ // takes care of odd population
mom = &(deme[ii]->select());
dad = &(deme[ii]->select());
pstats[ii].numsel += 2;
c1 = c2 = 0;
if(GAFlipCoin(pc)){
pstats[ii].numcro += (*scross)(*mom, *dad, &tmppop->individual(i),
&tmppop->individual(i+1));
c1 = c2 = 1;
}
else{
tmppop->individual( i ).copy(*mom);
tmppop->individual(i+1).copy(*dad);
}
pstats[ii].nummut += (mut=tmppop->individual( i ).mutate(pMutation()));
if(mut > 0) c1 = 1;
pstats[ii].nummut += (mut=tmppop->individual(i+1).mutate(pMutation()));
if(mut > 0) c2 = 1;
pstats[ii].numeval += c1 + c2;
}
if(nrepl[ii] % 2 != 0){ // do the remaining population member
mom = &(deme[ii]->select());
dad = &(deme[ii]->select());
pstats[ii].numsel += 2;
c1 = 0;
if(GAFlipCoin(pc)){
pstats[ii].numcro +=
(*scross)(*mom, *dad, &tmppop->individual(i), (GAGenome*)0);
c1 = 1;
}
else{
if(GARandomBit()) tmppop->individual(i).copy(*mom);
else tmppop->individual(i).copy(*dad);
}
pstats[ii].nummut += (mut=tmppop->individual(i).mutate(pMutation()));
if(mut > 0) c1 = 1;
pstats[ii].numeval += c1;
}
for(i=0; i<nrepl[ii]; i++)
deme[ii]->add(&tmppop->individual(i));
deme[ii]->evaluate();
deme[ii]->scale();
for(i=0; i<nrepl[ii]; i++)
tmppop->replace(deme[ii]->remove(GAPopulation::WORST,
GAPopulation::SCALED), i);
pstats[ii].numrep += nrepl[ii];
}
migrate();
for(unsigned int jj=0; jj<npop; jj++) {
deme[jj]->evaluate();
pstats[jj].update(*deme[jj]);
}
stats.numsel = stats.numcro = stats.nummut = stats.numrep = stats.numeval=0;
for(unsigned int kk=0; kk<npop; kk++) {
pop->individual(kk).copy(deme[kk]->best());
stats.numsel += pstats[kk].numsel;
stats.numcro += pstats[kk].numcro;
stats.nummut += pstats[kk].nummut;
stats.numrep += pstats[kk].numrep;
stats.numeval += pstats[kk].numeval;
}
pop->touch();
stats.update(*pop);
for(unsigned int ll=0; ll<npop; ll++)
stats.numpeval += pstats[ll].numpeval;
}