static void Run3SATSolver(string filePath, GeneticOptions options, bool verbose = false)
{
var instance = SATInstanceLoader.LoadInstance(filePath);
var problem = new ThreeSATProblem(instance.Formula);
var stopwatch = new Stopwatch();
GAResult result = null;
double relErr = 0;
double time = 0;
for (int i = 0; i < MeasurementCount; i++)
{
stopwatch.Reset();
stopwatch.Start();
result = GeneticAlgorithm.Solve(problem, options, verbose);
stopwatch.Stop();
time += stopwatch.Elapsed.TotalMilliseconds;
relErr = CalculateRelativeError(instance.OptimalWeight, result.Result.Fitness);
}
if (verbose)
{
Console.WriteLine();
PrintConfig(result);
}
var relativeErr = CalculateRelativeError(instance.OptimalWeight, result.Result.Fitness) * 100;
if (verbose)
{
Console.WriteLine("Optimal result is '{0}' and approx. '{1}'. Relative error is '{2}%'.", instance.OptimalWeight, result.Result.Fitness, relativeErr);
}
if (verbose)
{
Console.WriteLine();
}
time /= MeasurementCount;
Console.WriteLine(FormatCsvResult(instance.InstanceId, time, relErr));
}
public void FitnessFunctionTest()
{
var formula = new Formula(3, 3);
formula.Clauses = new int[]
{
1, 2, 3,
-1, -2, -3,
-1, -2, -3
};
formula.Weights = new int[]
{
1, 2, 3
};
var problem = new ThreeSATProblem(formula);
var expectedWinner = new Individual(new bool[]
{
false, true, true
});
var initialPopulation = new List <Individual>
{
new Individual(new bool[]
{
false, false, true
}),
new Individual(new bool[]
{
true, true, true
}),
expectedWinner
};
var population = new Population(100, 2, problem, initialPopulation);
population.MakeSelection(3);
var winner = population.GetFittestIndividual();
Assert.Equal(expectedWinner, winner);
}
