void Photon::HopDropSpin(MCMLModel * model) {
if ((model->layerObj.layer[layer].mua == 0.0) &&
(model->layerObj.layer[layer].mus == 0.0))
// glass layer
HopInGlass(model);
else
HopDropSpinInTissue(model);
if ((w < model->Wth) && dead)
Roulette();
}
/**
* C-routine to reach a floatvalue using a genetic model<br>
* bugs: none found<br>
* @param target floatvalue that serves as a target<br>
* @param maxiter maximum number of itterations<br>
*/
int SolveNumber(float target, int maxiter){
while (maxiter > 0){
//storage for our population of chromosomes.
bytechromosome Population[POP_SIZE];
//first create a random population, all with zero fitness.
for (int i=0; i<POP_SIZE; i++){
Population[i].setbits(GetRandomBits(CHROMO_LENGTH));
Population[i].setfitness(0.0f);
}
int GenerationsRequiredToFindASolution = 0;
//we will set this flag if a solution has been found
bool bFound = false;
//enter the main GA loop
while(!bFound){
//this is used during roulette wheel sampling
float TotalFitness = 0.0f;
// test and update the fitness of every chromosome in the population
for (int i=0; i<POP_SIZE; i++){
Population[i].setfitness(AssignFitness(Population[i].getbits(), (int)target));
TotalFitness += Population[i].getfitness();
}
// check to see if we have found any solutions (fitness will be 999)
for (int i=0; i<POP_SIZE; i++){
if (Population[i].getfitness() == 999.0f){
printf("Solution found in %d generations!\n",GenerationsRequiredToFindASolution);
PrintChromo(Population[i].getbits());
bFound = true;
break;
}
}
// create a new population by selecting two parents at a time and creating offspring
// by applying crossover and mutation. Do this until the desired number of offspring
// have been created.
//define some temporary storage for the new population we are about to create
bytechromosome temp[POP_SIZE];
int cPop = 0;
//loop until we have created POP_SIZE new chromosomes
while (cPop < POP_SIZE){
// we are going to create the new population by grabbing members of the old population
// two at a time via roulette wheel selection.
string offspring1 = Roulette((int)TotalFitness, Population);
string offspring2 = Roulette((int)TotalFitness, Population);
//add crossover dependent on the crossover rate
Crossover(offspring1, offspring2);
//now mutate dependent on the mutation rate
Mutate(offspring1);
Mutate(offspring2);
//add these offspring to the new population. (assigning zero as their fitness scores)
temp[cPop++] = bytechromosome(offspring1, 0.0f);
temp[cPop++] = bytechromosome(offspring2, 0.0f);
}//end loop
//copy temp population into main population array
for (int i=0; i<POP_SIZE; i++){
Population[i] = temp[i];
}
GenerationsRequiredToFindASolution++;
if (GenerationsRequiredToFindASolution > MAX_ALLOWABLE_GENERATIONS){
printf("No solutions found this run!\n");
bFound = true;
}
}
printf("\n");
maxiter--;
}//end while
return 0;
}
