//--------------------------------Roulette-------------------------------------------
//
// selects a chromosome from the population via roulette wheel selection
//------------------------------------------------------------------------------------
private static string Roulette(float total_fitness, chromo[] Population)
{
//generate a random number between 0 & total fitness count
float Slice = (float)(r.NextDouble() * total_fitness);
//go through the chromosones adding up the fitness so far
float FitnessSoFar = 0.0f;
for (int i = 0; i < POP_SIZE; i++)
{
FitnessSoFar += Population[i].fitness;
//if the fitness so far > random number return the chromo at this point
if (FitnessSoFar >= Slice)
return Population[i].bits;
}
return "";
}
static void Main(string[] args)
{
//seed the random number generator
r = new Random();
//just loop endlessly until user gets bored :0)
while (true)
{
//storage for our population of chromosomes.
chromo[] Population = new chromo[POP_SIZE];
//get a target number from the user. (no error checking)
float Target;
System.Console.Out.WriteLine("\nInput a target number: ");
Target = float.Parse(System.Console.In.ReadLine());
//first create a random population, all with zero fitness.
for (int i = 0; i < POP_SIZE; i++)
{
Population[i] = new chromo(GetRandomBits(CHROMO_LENGTH), 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].fitness = AssignFitness(Population[i].bits, Target);
TotalFitness += Population[i].fitness;
}
// check to see if we have found any solutions (fitness will be 999)
for (int i = 0; i < POP_SIZE; i++)
{
if (Population[i].fitness == 999.0f)
{
System.Console.Out.WriteLine("\nSolution found in " + GenerationsRequiredToFindASolution + " generations!");
PrintChromo(Population[i].bits);
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
chromo[] temp = new chromo[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(TotalFitness, Population);
string offspring2 = Roulette(TotalFitness, Population);
//add crossover dependent on the crossover rate
Crossover(ref offspring1, ref offspring2);
//now mutate dependent on the mutation rate
Mutate(ref offspring1);
Mutate(ref offspring2);
//add these offspring to the new population. (assigning zero as their
//fitness scores)
temp[cPop++] = new chromo(offspring1, 0.0f);
temp[cPop++] = new chromo(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;
// exit app if no solution found within the maximum allowable number
// of generations
if (GenerationsRequiredToFindASolution > MAX_ALLOWABLE_GENERATIONS)
{
System.Console.Out.WriteLine("No solutions found this run!");
bFound = true;
}
}
}//end while
}
