int main(int argc, char** argv)
{
Timer mainTimer(Timer::Mode::Single);
mainTimer.Start();
int population, generation, elite, numberOfThreads;
char *imageFile;
if(argc != 6)
{
printf("Wrong number of arguments, correct number is: 1 population, 2 generation, 3 elite, 4 image file\n");
return 0;
}
population = atoi(argv[1]);
generation = atoi(argv[2]);
elite = atoi(argv[3]);
imageFile = argv[4];
numberOfThreads = atoi(argv[5]);
GeneticAlgorithm *geneticAlgorithm = new GeneticAlgorithm(population, generation, elite, imageFile, numberOfThreads);
geneticAlgorithm->Calculate();
delete geneticAlgorithm;
mainTimer.Stop();
printf("%lu,", mainTimer.Get());
return 0;
}
void Render::updateGraph() {
GeneticAlgorithm *algorithm = world->getAlgorithm();
int count = algorithm->getGenerationNum();
if (!count)
return;
if (avgScore) {
delete []avgScore;
delete []maxScore;
}
int width = this->width() - 400;
int height = 200;
float dx = float(width)/count;
float max = algorithm->getMaxScore(count);
if (max < 1)
max = 1;
count++;
avgScore = new float[count*2];
maxScore = new float[count*2];
for (int i = 0; i < count; i++) {
avgScore[i*2] = 200 + i*dx;
avgScore[i*2 + 1] = 230 - height*(algorithm->getAvgScore(i)/max);
maxScore[i*2] = 200 + i*dx;
maxScore[i*2 + 1] = 230 - height*(algorithm->getMaxScore(i)/max);
}
}
int main (int argc, char * argv[])
{
if (argc < 2)
{
showUsage ();
return 0;
}
if (string (argv[1]) == "-l")
{
unsigned int nof_beams = 0;
if (argc < 3)
{
showUsage ();
return 0;
}
else
{
nof_beams = atoi (argv[2]);
}
LocalBeam hc (nof_beams);
LBMultiplier * multiplier = hc.bestMultiplier ();
LBMultiplier * solution;
solution = multiplier;
cout << "Solution: " << endl;
cout << solution->toString () << endl;
set<pair<unsigned int, unsigned int> > correct_cases =
solution->getCorrectAnswers ();
for (set<pair<unsigned int, unsigned int> >::iterator it = correct_cases.begin ();
it != correct_cases.end (); ++it)
cout << it->first << " x " << it->second << endl;
}
else if (string (argv[1]) == "-g")
{
GeneticAlgorithm ga (300);
GAMultiplier * multiplier = ga.bestMultiplier ();
GAMultiplier * solution;
solution = multiplier;
cout << "Solution: " << endl;
cout << solution->toString () << endl;
set<pair<unsigned int, unsigned int> > correct_cases =
solution->getCorrectAnswers ();
for (set<pair<unsigned int, unsigned int> >::iterator it = correct_cases.begin ();
it != correct_cases.end (); ++it)
cout << it->first << " x " << it->second << endl;
delete solution;
}
return 0;
}
int main()
{
GeneticAlgorithm ga;
ga.run();
bool pause;
cin >> pause;
return 0;
}
void tryReadFremExistMusicFile(const string& music_file_name)
{
readFromFile(music_file_name);
Main_Genetic_Algorithm.setMaxGeneration(Max_Generation);
Main_Genetic_Algorithm.setCrossoverRate(Crossover_Rate);
Main_Genetic_Algorithm.setMutationRate(Mutation_Rate);
Main_Genetic_Algorithm.setIndexElitismIndividual(Index_Elitism_Individual);
}
void GeneticAlgorithmTest::test_from_XML(void)
{
message += "test_from_XML\n";
GeneticAlgorithm ga;
tinyxml2::XMLDocument* document = ga.to_XML();
ga.from_XML(*document);
delete document;
}
void GeneticAlgorithmTest::test_to_XML(void)
{
message += "test_to_XML\n";
GeneticAlgorithm ga;
tinyxml2::XMLDocument* document = ga.to_XML();
assert_true(document != NULL, LOG);
delete document;
}
void Render::createCarCallList() {
GeneticAlgorithm *algorithm = world->getAlgorithm();
unsigned int oldCallListNubmer = algorithm->getCarCallListNuber();
if (oldCallListNubmer)
glDeleteLists(oldCallListNubmer, 1);
unsigned int listNubmer = glGenLists(1);
algorithm->setCarCallList(listNubmer);
glNewList(listNubmer, GL_COMPILE);
drawCar();
glEndList();
}
int main (void)
{
ShootingField myShooter;
GeneticAlgorithm myPopulation;
int targetX;
int targetY;
myShooter.clearField();
myPopulation.runGeneticAlgo(myShooter.myTarget->TargetLocX, myShooter.myTarget->TargetLocY);
return 0;
}
void main()
{ // Creates the algorithm object, initialise the methods to be used this run
// of the genetic algorithm then call the main running of the genetic
// algorithm loop
GeneticAlgorithm theGA; // Create algorithm object
bool isSuccess = true; // Algorithm can solve
theGA.setup(&isSuccess); // Read algorithms settings
if (isSuccess == true)
{ // If everything loaded fine or user wants to use defaults, run algorithm
theGA.runGA();
}
} // main()
int main(int argc, char *argv[])
{
const string music_file_name = "Music";
readMusicFileOrNewAlgorithm(music_file_name);
Main_Genetic_Algorithm.run();
outputToFile(music_file_name);
writeToPy();
return EXIT_SUCCESS;
}
void Render::drawGraph() {
GeneticAlgorithm *algorithm = world->getAlgorithm();
int count = algorithm->getGenerationNum();
if (!count)
return;
if (genrationNum != count) {
genrationNum = count;
updateGraph();
}
count++;
glLineWidth(2);
qglColor(Qt::black);
glVertexPointer(2, GL_FLOAT, 0, avgScore);
glEnableClientState(GL_VERTEX_ARRAY);
glDrawArrays(GL_LINE_STRIP, 0, count);
qglColor(Qt::red);
glVertexPointer(2, GL_FLOAT, 0, maxScore);
glDrawArrays(GL_LINE_STRIP, 0, count);
glDisableClientState(GL_VERTEX_ARRAY);
glLineWidth(1);
}
void Render::drawTable() {
int stepY = qMin((height() - 1)/33, 14);
qglColor(QColor(255, 255, 200, 192));
glBegin(GL_QUADS);
glVertex2f(5, 1);
glVertex2f(135, 1);
glVertex2f(135, 33*stepY);
glVertex2f(5, 33*stepY);
glEnd();
qglColor(Qt::gray);
glVertexPointer(2, GL_FLOAT, 0, tableBoard);
glEnableClientState(GL_VERTEX_ARRAY);
glDrawArrays(GL_LINES, 0, 76);
glDisableClientState(GL_VERTEX_ARRAY);
qglColor(Qt::black);
font.setPixelSize(12);
QFontMetrics fm(font);
int y = 2 + fm.ascent();
renderText(15, y, "#", font);
renderText(40, y, "Score", font);
renderText(90, y, "Time", font);
y += stepY;
GeneticAlgorithm *algorithm = world->getAlgorithm();
for (int i = 0; i < 32; i++) {
qglColor(Qt::black);
if (!algorithm->getCarNum() && algorithm->getGenerationNum()) {
switch (algorithm->getOffspringsCount(i)) {
case 0: qglColor(Qt::red);
break;
case 1: qglColor(Qt::black);
break;
case 2: qglColor(Qt::darkGreen);
break;
}
}
renderText(15, y + i*stepY, QString::number(i), font);
float score = algorithm->getScore(i);
if (score >= 0) {
renderText(40, y + i*stepY, QString::number(score, '0', 1), font);
int ss = algorithm->getTime(i);
QTime time(0, ss/60, ss%60);
renderText(90, y + i*stepY, time.toString("m:ss"), font);
}
}
}
int main( int argc, char ** argv ) {
std::string *filename = NULL;
int times = 0, cost = 0, innerTimes = 0;
bool mazeValid = false;
FileHandler *file = NULL;
Maze *maze = new Maze( ROWS, COLS );
GeneticAlgorithm *algorithm = new GeneticAlgorithm( maze );
Search *search = new Search( maze );
Agent *agent = new Agent( maze );
if( argc == 2 ) {
filename = new std::string( argv[1] );
} else {
filename = new std::string( "maze.txt" );
}
algorithm->setMinIterations( MIN_ITERATIONS );
algorithm->setMutationRate( MUTATION_RATE );
algorithm->setCrossOverRate( CROSS_OVER_RATE );
maze->setRandomRate( RANDOM_RATE );
maze->generateFirstPopulation();
do {
times++;
algorithm->run();
mazeValid = maze->searchDoors();
// Se ele nao tem portas suficientes para testar
// Executa o A.G denovo
if( ! mazeValid ) {
continue;
}
while( ( mazeValid = maze->selectDoors( MIN_COST, MAX_COST ) ) ) {
// Se a menor distancia entre a entrada e saida for maior
// que o MAX_COST, entao nao ha caminho valido
if( ! maze->validDistance( MIN_COST, MAX_COST ) ) {
continue;
}
cost = search->hasSolution( true ); // Usa o algoritmo A*, mais rapido a convergencia...
if( cost >= MIN_COST && cost <= MAX_COST ) {
break;
}
innerTimes++;
}
if( times % 10 == 0 ) {
std::cout << times << ": " << DataNode::ctor << ", " << DataNode::dtor << std::endl;
}
} while( ! mazeValid );
agent->run();
try {
file = new FileHandler( filename->c_str() );
file->printMaze( maze );
std::cout << "Maze dump in " << *filename << std::endl;
} catch( int e ) {
std::cout << file->getErrorMessage();
}
delete agent;
delete search;
delete maze;
delete filename;
delete algorithm;
delete file;
return 0;
}
/**
* Entry point for the program.
*/
int main(int argc, char **argv) {
GeneticAlgorithm ga;
double xs[N];
double mutationRate, crossoverProbability, randomSeed, faithDegree;
double bounds[N][2];
int maxGenerations, populationSize;
printf("Population size: ");
scanf("%d", &populationSize);
printf("Maximum number of generations: ");
scanf("%d", &maxGenerations);
printf("Mutation rate (0..1): ");
scanf("%lf", &mutationRate);
printf("Crossover probability (0..1): ");
scanf("%lf", &crossoverProbability);
printf("Faith degree (0..1): ");
scanf("%lf", &faithDegree);
printf("Random seed: ");
scanf("%lf", &randomSeed);
#ifdef F1
for (int i = 0; i < N; i++) {
bounds[i][0] = 0;
bounds[i][1] = 10;
}
#else
#ifdef F2
bounds[0][0] = 0;
bounds[0][1] = 1200;
bounds[1][0] = 0;
bounds[1][1] = 1200;
bounds[2][0] = -0.55;
bounds[2][1] = 0.55;
bounds[3][0] = -0.55;
bounds[3][1] = 0.55;
#else
#ifdef F3
for (int i = 0; i < 2; i++) {
bounds[i][0] = -2.3;
bounds[i][1] = 2.3;
}
for (int i = 2; i < N; i++) {
bounds[i][0] = -3.2;
bounds[i][1] = 3.2;
}
#endif
#endif
#endif
// Solve problem
ga.setCrossoverProbability(crossoverProbability);
ga.setMutationProbability(mutationRate);
ga.setElitism(true);
#ifdef F1
ga.setMinimizer(false);
#else
ga.setMinimizer(true);
#endif
ga.setFaithDegree(faithDegree);
ga.setRepresentationType(RepresentationType::REAL_REPRESENTATION);
ga.setSelectionType(SelectionType::BOLTZMANN_SELECTION);
ga.setCrossoverType(CrossoverType::SIMULATED_BINARY_CROSSOVER);
ga.setMutationType(MutationType::PARAMETER_BASED_MUTATION);
ga.solve(xs, N, populationSize, bounds, NULL, maxGenerations, randomSeed, f, g, NG, h, NH);
double fxs = f(xs);
printf("x* = (%.10f", xs[0]);
for (int i = 1; i < N; i++)
printf(", %.10f", xs[i]);
printf(")\nf(x*) = %.10f\n", fxs);
return EXIT_SUCCESS;
}
int main(int argc, char* argv[])
{
srand (time(NULL));
SequenceDiagram seqD("C:/Users/Abhinav/Desktop/BTP/Model Based Testing/test.xml");
//seqD.display();
// Generate List of conditions
for(map<string, CombinedFragment*>::iterator i = Message_List.begin(); i!=Message_List.end(); i++)
{
if(i->second){
string id = i->second->getID();
if((!Num_Part[id]) || Num_Part[id] < Partitions[i->first] )
Num_Part[id] = Partitions[i->first];
}
}
for(map<string,int>::iterator i =Num_Part.begin(); i!=Num_Part.end();i++)
for(int j =1; j<=i->second;j++)
{
ostringstream temp;
temp<<j;
SeqD_conditions.push_back(i->first+(temp.str()));
}
// Condition Vector Generated
/*for(unsigned int i =0; i<SeqD_conditions.size();i++)
cout<<SeqD_conditions[i]<<endl;
*/
// Create List of Test Sequences
SequenceDiagramTester sdt(seqD);
vector<TestSequence> TSList =sdt.generate();
sort(TSList.begin(), TSList.end());
for(unsigned int i =0; i<TSList.size(); i++)
{
TSList[i].evalConditions();
TSList[i].setNum(i);
}
//Test Sequence List created
/*
Chromosome C;
C.generate(TSList,2);
cout<<" "<<C.getWeight()<<" "<<C.getFitness()<<" "<<C.getMessageCount()<<endl<<endl;;
*/
/*map<string,int> t = C.getConditionHash();
for(map<string,int>::iterator i =t.begin(); i!=t.end(); i++)
cout<<i->first<<endl;*/
/*for(unsigned int i =0; i<TSList.size(); i++)
{
cout<<"TS : "<<TSList[i].getPriority()<<" ";
vector<Message> tt = TSList[i].getTestSequence();
for(unsigned j=0; j<tt.size(); j++)
cout<<tt[j].getName()<<" ";
cout<<endl;
}*/
/*
for(unsigned int i =0; i<temp.size(); i++)
{
cout<<"TS-"<<temp[i].getNum()<<" : "<<temp[i].getPriority()<<" ";
vector<Message> tt = temp[i].getTestSequence();
for(unsigned j=0; j<tt.size(); j++)
cout<<tt[j].getName()<<" ";
cout<<endl;
}
*/
/*ChromosomeBuilder C;
C.generate(TSList,5);
vector<Chromosome> lis = C.getPopulation();
for(unsigned s = 0; s<2; s++)
{
cout<<" "<<lis[s].getCount()<<" "<<lis[s].getFitness()<<endl<<endl;;
vector<TestSequence> temp =lis[s].getSequence();
for(unsigned int i =0; i<temp.size(); i++)
{
cout<<"TS-"<<temp[i].getNum()<<" : "<<temp[i].getPriority()<<" ";
vector<Message> tt = temp[i].getTestSequence();
for(unsigned j=0; j<tt.size(); j++)
cout<<tt[j].getName()<<" ";
cout<<endl;
}
}
cout<<endl<<endl<<"Genetic Algo "<<endl;
GeneticAlgorithm GA;
vector<Chromosome> t =GA.crossover(lis[0],lis[1]);
sort(t.begin(),t.end());
for(unsigned s = 0; s<t.size(); s++)
{
cout<<" "<<t[s].getCount()<<" "<<t[s].getFitness()<<endl<<endl;;
vector<TestSequence> temp =t[s].getSequence();
for(unsigned int i =0; i<temp.size(); i++)
{
cout<<"TS-"<<temp[i].getNum()<<" : "<<temp[i].getPriority()<<" ";
vector<Message> tt = temp[i].getTestSequence();
for(unsigned j=0; j<tt.size(); j++)
cout<<tt[j].getName()<<" ";
cout<<endl;
}
}
t =GA.mutation(t);
for(unsigned s = 0; s<t.size(); s++)
{
cout<<" "<<t[s].getCount()<<" "<<t[s].getFitness()<<endl<<endl;;
vector<TestSequence> temp =t[s].getSequence();
for(unsigned int i =0; i<temp.size(); i++)
{
cout<<"TS-"<<temp[i].getNum()<<" : "<<temp[i].getPriority()<<" ";
vector<Message> tt = temp[i].getTestSequence();
for(unsigned j=0; j<tt.size(); j++)
cout<<tt[j].getName()<<" ";
cout<<endl;
}
}*/
GeneticAlgorithm GA;
GA.generate(TSList,5);
return 0;
}
