HANDLE WINAPI OpenW(const OpenInfo* info)
{
if (!info || info->StructSize < sizeof(OpenInfo))
return nullptr;
//Determine file name to analyze
wstring file_name;
if (info->OpenFrom == OPEN_COMMANDLINE && info->Data) {
const OpenCommandLineInfo* ocli = reinterpret_cast<const OpenCommandLineInfo*>(info->Data);
if (!ocli || ocli->StructSize < sizeof(OpenCommandLineInfo) || !ocli->CommandLine || !ocli->CommandLine[0])
return nullptr;
//Get command line
wstring cmd_line = ocli->CommandLine;
size_t pos = 0;
while ((pos = cmd_line.find(L'\"', pos)) != string::npos)
cmd_line.erase(pos, 1);
while (!cmd_line.empty() && iswspace(cmd_line[0]))
cmd_line.erase(0, 1);
while (!cmd_line.empty() && iswspace(cmd_line[cmd_line.length() - 1]))
cmd_line.erase(cmd_line.length() - 1, 1);
if (cmd_line.empty())
return nullptr;
//Expand environment variables in path string
wstring exp_path(2048, 0);
if (ExpandEnvironmentStrings(cmd_line.c_str(), &exp_path.front(), static_cast<DWORD>(exp_path.size() - 1)))
exp_path.resize(lstrlen(exp_path.c_str()));
else
exp_path = cmd_line;
const size_t path_len = _FSF.ConvertPath(CPM_FULL, exp_path.c_str(), nullptr, 0);
if (path_len) {
file_name.resize(path_len);
_FSF.ConvertPath(CPM_FULL, exp_path.c_str(), &file_name[0], path_len);
}
}
else if (info->OpenFrom == OPEN_PLUGINSMENU) {
PanelInfo pi;
ZeroMemory(&pi, sizeof(pi));
pi.StructSize = sizeof(pi);
if (!_PSI.PanelControl(PANEL_ACTIVE, FCTL_GETPANELINFO, 0, &pi))
return nullptr;
const intptr_t ppi_len = _PSI.PanelControl(PANEL_ACTIVE, FCTL_GETPANELITEM, static_cast<int>(pi.CurrentItem), nullptr);
if (ppi_len == 0)
return nullptr;
vector<unsigned char> buffer(ppi_len);
PluginPanelItem* ppi = reinterpret_cast<PluginPanelItem*>(&buffer.front());
FarGetPluginPanelItem fgppi;
ZeroMemory(&fgppi, sizeof(fgppi));
fgppi.StructSize = sizeof(fgppi);
fgppi.Size = buffer.size();
fgppi.Item = ppi;
if (!_PSI.PanelControl(PANEL_ACTIVE, FCTL_GETPANELITEM, static_cast<int>(pi.CurrentItem), &fgppi))
return nullptr;
const size_t file_name_len = _FSF.ConvertPath(CPM_FULL, ppi->FileName, nullptr, 0);
if (file_name_len) {
file_name.resize(file_name_len);
_FSF.ConvertPath(CPM_FULL, ppi->FileName, &file_name[0], file_name_len);
}
}
if (!file_name.empty()) {
unlocker u;
u.process(file_name.c_str());
}
return nullptr;
}
int main(int argc, char **argv)
{
std::chrono::time_point<std::chrono::system_clock> program_start, program_end;
program_start = std::chrono::system_clock::now();
// MPI init - this has to happen before getopt() so the argv can be properly trimmed
MPI_Init(&argc, &argv);
MPI_Comm_size(MPI_COMM_WORLD, &mpi_tasks);
MPI_Comm_rank(MPI_COMM_WORLD, &mpi_rank);
char hostname[1024];
hostname[1023] = '\0';
gethostname(hostname, 1023);
srand(time(NULL));
double mutation_rate = 0.01;
double crossover_rate = 0.01;
int population_size = 10;
int n_generations = 10;
char c='h';
// Handle command line arguments
while ((c = getopt (argc, argv, "t:g:p:c:m:s:h:e:x:")) != -1)
switch (c)
{
case 'x':
experiment_path = optarg;
break;
case 't':
n_trials = atoi(optarg);
break;
case 'g':
n_generations = atoi(optarg);
break;
case 'p':
population_size = atoi(optarg);
break;
case 'm':
mutation_rate = strtod(optarg, NULL);
break;
case 'c':
crossover_rate = strtod(optarg, NULL);
break;
case 's':
mutation_stdev = strtod(optarg, NULL);
break;
case 'e':
elitism = atoi(optarg);
break;
case 'h':
printf("Usage: %s -p {population size} -g {number of generations} -t {number of trials} -c {crossover rate} -m {mutation rate} -s {mutation standard deviation} -e {elitism 0 or 1}", argv[0]);
break;
case '?':
if (optopt == 'p')
fprintf (stderr, "Option -%c requires an argument specifying the population size.\n", optopt);
else if (optopt == 'g')
fprintf (stderr, "Option -%c requires an argument specifying the number of generations.\n", optopt);
else if (optopt == 't')
fprintf (stderr, "Option -%c requires an argument specifying the number of trials.\n", optopt);
else if (optopt == 'c')
fprintf (stderr, "Option -%c requires an argument specifying the crossover rate.\n", optopt);
else if (optopt == 'm')
fprintf (stderr, "Option -%c requires an argument specifying the mutation rate.\n", optopt);
else if (optopt == 's')
fprintf (stderr, "Option -%c requires an argument specifying the mutation standard deviation.\n", optopt);
else if (isprint (optopt))
fprintf (stderr, "Unknown option `-%c'.\n", optopt);
else
fprintf (stderr,
"Unknown option character `\\x%x'.\n",
optopt);
return 1;
default:
printf("Usage: %s -p {population size} -g {number of generations} -t {number of trials} -c {crossover rate} -m {mutation rate} -s {mutation standard deviation}", argv[0]);
abort ();
}
if (experiment_path.empty())
{
printf("Usage: %s -p {population size} -g {number of generations} -t {number of trials} -c {crossover rate} -m {mutation rate} -s {mutation standard deviation} -x {argos experiment file}\n", argv[0]);
exit(1);
}
float max_float = std::numeric_limits<float>::max();
//printf("%s:\tworker %d ready.\n", hostname, mpi_rank);
// See if we've been given a seed to use (for testing purposes). When you
// specify a random seed, the evolution will be exactly the same each time
// you use that seed number
unsigned int seed = 12345;
for(int i=1 ; i<argc ; i++)
if(strcmp(argv[i++],"seed") == 0)
seed = atoi(argv[i]);
// popsize / mpi_tasks must be an integer
population_size = mpi_tasks * int((double)population_size/(double)mpi_tasks+0.999);
if (mpi_rank==0)
{
printf("Population size: %d\nNumber of trials: %d\nNumber of generations: %d\nCrossover rate: %f\nMutation rate: %f\nMutation stdev: %f\nAllocated MPI workers: %d\n", population_size, n_trials, n_generations, crossover_rate, mutation_rate, mutation_stdev, mpi_tasks);
printf("elitism: %d\n", elitism);
}
// Define the genome
GARealAlleleSetArray allele_array;
allele_array.add(0, 1.0); // Probability of switching to search
allele_array.add(0, 1.0); // Probability of returning to nest
allele_array.add(0, 4*M_PI); // Uninformed search variation
allele_array.add(0, 20); // Rate of informed search decay
allele_array.add(0, 20); // Rate of site fidelity
allele_array.add(0, 20); // Rate of laying pheremone
allele_array.add(0, 20); // Rate of pheremone decay
// Create the template genome using the phenotype map we just made.
GARealGenome genome(allele_array, objective);
genome.crossover(GARealUniformCrossover);
genome.mutator(GARealGaussianMutatorStdev); // Specify our version of the Gaussuan mutator
genome.initializer(CPFAInitializer);
// Now create the GA using the genome and run it.
GASimpleGA ga(genome);
GALinearScaling scaling;
ga.maximize(); // Maximize the objective
ga.populationSize(population_size);
ga.nGenerations(n_generations);
ga.pMutation(mutation_rate);
ga.pCrossover(crossover_rate);
ga.scaling(scaling);
if (elitism == 1)
ga.elitist(gaTrue);
else
ga.elitist(gaFalse);
if(mpi_rank == 0)
ga.scoreFilename("evolution.txt");
else
ga.scoreFilename("/dev/null");
ga.recordDiversity(gaTrue);
ga.scoreFrequency(1);
ga.flushFrequency(1);
ga.selectScores(GAStatistics::AllScores);
// Pass MPI data to the GA class
ga.mpi_rank(mpi_rank);
ga.mpi_tasks(mpi_tasks);
//ga.evolve(seed); // Manual generations
// initialize the ga since we are not using the evolve function
ga.initialize(seed); // This is essential for the mpi workers to be sychronized
// Name the results file with the current time and date
time_t t = time(0); // get time now
struct tm * now = localtime( & t );
stringstream ss;
boost::filesystem::path exp_path(experiment_path);
ss << "results/CPFA-evolution-"
<< exp_path.stem().string() << '-'
<<GIT_BRANCH<<"-"<<GIT_COMMIT_HASH<<"-"
<< (now->tm_year) << '-'
<< (now->tm_mon + 1) << '-'
<< now->tm_mday << '-'
<< now->tm_hour << '-'
<< now->tm_min << '-'
<< now->tm_sec << ".csv";
string results_file_name = ss.str();
if (mpi_rank == 0)
{
// Write output file header
ofstream results_output_stream;
results_output_stream.open(results_file_name, ios::app);
results_output_stream << "Population size: " << population_size << "\nNumber of trials: " << n_trials << "\nNumber of generations: "<< n_generations<<"\nCrossover rate: "<< crossover_rate<<"\nMutation rate: " << mutation_rate << "\nMutation stdev: "<< mutation_stdev << "Algorithm: CPFA\n" << "Number of searchers: 6\n" << "Number of targets: 256\n" << "Target distribution: power law" << endl;
results_output_stream << "Generation"
<< ", " << "Compute Time (s)"
<< ", " << "Convergence"
<< ", " << "Mean"
<< ", " << "Maximum"
<< ", " << "Minimum"
<< ", " << "Standard Deviation"
<< ", " << "Diversity"
<< ", " << "ProbabilityOfSwitchingToSearching"
<< ", " << "ProbabilityOfReturningToNest"
<< ", " << "UninformedSearchVariation"
<< ", " << "RateOfInformedSearchDecay"
<< ", " << "RateOfSiteFidelity"
<< ", " << "RateOfLayingPheromone"
<< ", " << "RateOfPheromoneDecay";
results_output_stream << endl;
results_output_stream.close();
}
while(!ga.done())
{
std::chrono::time_point<std::chrono::system_clock>generation_start, generation_end;
if (mpi_rank == 0)
{
generation_start = std::chrono::system_clock::now();
}
// Calculate the generation
ga.step();
if(mpi_rank == 0)
{
generation_end = std::chrono::system_clock::now();
std::chrono::duration<double> generation_elapsed_seconds = generation_end-generation_start;
ofstream results_output_stream;
results_output_stream.open(results_file_name, ios::app);
results_output_stream << ga.statistics().generation()
<< ", " << generation_elapsed_seconds.count()
<< ", " << ga.statistics().convergence()
<< ", " << ga.statistics().current(GAStatistics::Mean)
<< ", " << ga.statistics().current(GAStatistics::Maximum)
<< ", " << ga.statistics().current(GAStatistics::Minimum)
<< ", " << ga.statistics().current(GAStatistics::Deviation)
<< ", " << ga.statistics().current(GAStatistics::Diversity);
for (int i = 0; i < GENOME_SIZE; i++)
results_output_stream << ", " << dynamic_cast<const GARealGenome&>(ga.population().best()).gene(i);
const GARealGenome& best_genome = dynamic_cast<const GARealGenome&>(ga.statistics().bestIndividual());
results_output_stream << endl;
results_output_stream << "The GA found an optimum at: ";
results_output_stream << best_genome.gene(0);
for (int i = 1; i < GENOME_SIZE; i++)
results_output_stream << ", " << best_genome.gene(i);
results_output_stream << " with score: " << best_genome.score();
results_output_stream << endl;
results_output_stream.close();
}
}
// Display the GA's progress
if(mpi_rank == 0)
{
cout << ga.statistics() << " " << ga.parameters() << endl;
}
MPI_Finalize();
program_end = std::chrono::system_clock::now();
std::chrono::duration<double> program_elapsed_seconds = program_end-program_start;
if(mpi_rank == 0)
printf("Run time was %f seconds\n", program_elapsed_seconds.count());
return 0;
}
