public object Clone()
{ // make a copy of self and return it in object
GAMember g1 = new GAMember(this.memSize, DistMatrix);
for (int i = 0; i < g1.memSize; i++)
{
g1.mem[i] = this.mem[i];
}
g1.memSize = this.memSize;
g1.fitness = this.fitness;
return(g1);
}
public TSGA(int popSz, int numCities, float mutRate, float crossRate,
int[,] distMat, ref object lockobj) // for mulithreaded version
{ // constructor
this.mutationRate = mutRate;
this.memSize = numCities + 1; // start and end city is 0
this.crossoverRate = crossRate;
DistMat = distMat;
this.pop = new GAMember[popSz];
for (int i = 0; i < popSz; i++)
{
pop[i] = new GAMember(memSize, distMat);
}
this.popSize = popSz; //60
this.lockobj = lockobj;
}
void RunGA()//NOT USED
{
//----------
ga.InitializePopulation();
ga.EvaluatePopulation();
Array.Sort(ga.pop); // sort population
//int bestFitness = ga.pop[0].fitness;
int bestFitness = ga[0].fitness; // because of indexer
lock (this)
{
bestMember = (GAMember)ga[0].Clone();
}
for (int j = 0; j < TSGAConstants.NumIterations; j++)
{
// each loop is one generation
ga.SelectPopulation();
ga.CrossoverPopulation();
ga.EvaluatePopulation();
Array.Sort(ga.pop); // sort population
lblStatus2.Text = ("Best cand:" + j.ToString() + " " + ga.pop[0].fitness.ToString() +
" " + ga.pop[0].ToString());
lock (this)
{
if (ga.pop[0].fitness < bestMember.fitness)
{
bestMember = (GAMember)ga[0].Clone();
}
}
ga.MutatePopulation();
ga.EvaluatePopulation();
Array.Sort(ga.pop); // sort population
if (ga.pop[0].fitness < bestFitness)
{
bestFitness = ga.pop[0].fitness;
lblStatus2.Text = bestFitness.ToString();
}
}
}
public void RunGA() // overall GA
{
//----------
InitializePopulation();
EvaluatePopulation(); //get fitness for every GAmember
Array.Sort(pop); // sort population by fitness
int bestFitness = pop[0].fitness; // because of indexer
bestMember = (GAMember)pop[0].Clone();
for (int j = 0; j < TSGAConstants.NumIterations; j++)//50000 iter.
{
// each loop is one generation
SelectPopulation(); //duplicate top 30% into worst 30%
CrossoverPopulation();
EvaluatePopulation();
Array.Sort(pop); // sort population
lock (lockobj)
{
if (pop[0].fitness < bestMember.fitness)
{
bestMember = (GAMember)pop[0].Clone();
}
iterationCount = j;
}
MutatePopulation();
EvaluatePopulation();
Array.Sort(pop); // sort population
lock (lockobj)
{
if (pop[0].fitness < bestFitness)
{
bestFitness = pop[0].fitness;
}
}
}
}
public TSGA(int popSz, int numCities, float mutRate, float crossRate,
int[,] distMat, ref object lockobj, ref object[] synchObjsA, ref object[] synchObjsB,
int[] dataready, int[] xchgdone, int threadNum) // for mulithreaded version
{ // constructor
this.mutationRate = mutRate;
this.memSize = numCities + 1; // start and end city is 0
this.crossoverRate = crossRate;
this.pop = new GAMember[popSz];
for (int i = 0; i < popSz; i++)
{
pop[i] = new GAMember(memSize, DistMat);
}
this.popSize = popSz;
DistMat = distMat;
this.lockobj = lockobj;
this.threadNum = threadNum;
this.synchObjsA = synchObjsA;
this.synchObjsB = synchObjsB;
this.dataready = dataready;
this.xchgdone = xchgdone;
}
public void RunGA_XCHG() // overall GA with XCHG capabilities
{
InitializePopulation();
EvaluatePopulation();
Array.Sort(pop); // sort population
int bestFitness = pop[0].fitness; // because of indexer
bestMember = (GAMember)pop[0].Clone();
for (int j = 0; j < TSGAConstants.NumIterations; j++)
{
// each loop is one generation
SelectPopulation();
CrossoverPopulation();
EvaluatePopulation();
Array.Sort(pop); // sort population
lock (lockobj)
{
if (pop[0].fitness < bestMember.fitness)
{
bestMember = (GAMember)pop[0].Clone();
}
iterationCount = j;
}
if (((j + 1) % TSGAConstants.ExchangeAfterIterations) == 0) // xchange pop every few hundred(500) generations
{
lock (lockobj)
{
dataready[threadNum] = 1; // data is ready
}
lock (synchObjsA[threadNum])
{
Monitor.PulseAll(synchObjsA[threadNum]); // main will xchg and signal back
}
if (xchgdone[threadNum] == 0)
{
lock (synchObjsB[threadNum])
{
Monitor.Wait(synchObjsB[threadNum]); // wait for main to get done with xchg
}
}
lock (lockobj)
{
xchgdone[threadNum] = 0;
}
}
MutatePopulation();
EvaluatePopulation();
Array.Sort(pop); // sort population
lock (lockobj)
{
if (pop[0].fitness < bestFitness)
{
bestFitness = pop[0].fitness;
}
}
}
}
public int CompareTo(Object rhs) // for sorting
{
GAMember mr = (GAMember)rhs;
return(this.fitness.CompareTo(mr.fitness)); // index 0 is best
}
