static void Main(string[] args)
{
TSPFileLoader fileLoader = new TSPFileLoader();
fileLoader.LoadAndListTSPFiles();
TSPProblem problem = fileLoader.SelectAndLoadFile();
//problem.PrintDistanceMatrix();
Console.WriteLine("\nRandom Algorithm");
Algorithm randomAlgorithm = new RandomAlgorithm(problem);
randomAlgorithm.PerformAlgorithm();
printAlgorithmResults(randomAlgorithm);
Console.WriteLine("\nGreedy Algorithm");
Algorithm greedyAlgorithm = new GreedyAlgorithm(problem);
greedyAlgorithm.PerformAlgorithm();
printAlgorithmResults(greedyAlgorithm);
GeneticAlgorithmParameters parameters = readParameters();
Algorithm geneticAlgorithm = new GeneticAlgorithm(problem)
{
Parameters = parameters,
Selection = new TournamentSelection(),
Crossover = new OrderedCrossover(),
Mutation = new InversionMutation()
};
geneticAlgorithm.PerformAlgorithm();
printAlgorithmResults(geneticAlgorithm);
}
public void PerformAlgorithm(GeneticAlgorithmParameters parameters)
{
GeneticAlgortihmLogger logger = new GeneticAlgortihmLogger(parameters, problem.ProblemName);
population = generateRandomPopulation(parameters.PopulationSize);
//population = generateGreedyPopulation(parameters.PopulationSize);
BestIndividual = population[0];
evaluatePopulation();
logger.AppendLine(0, population);
for (int i = 1; i <= parameters.Generations; i++)
{
population = generateNewPopulation(parameters.PopulationSize, parameters.CrossoverProb, parameters.TournamentSize);
mutatePopulation(parameters.MutationProb);
evaluatePopulation();
logger.AppendLine(i, population);
}
logger.WriteToFile();
}
public GeneticAlgortihmLogger(GeneticAlgorithmParameters parameters, string problemName)
{
this.parameters = parameters;
this.problemName = problemName;
csv = new StringBuilder();
}
