public TrainEA CreateGA(int cityCount, BasicPopulation population, TravellingSalesManFitnessScore tspFitness, double crossOverProbabality, double mutationProbabality)
{
try
{
var geneticAlgorithm = new TrainEA(population, tspFitness);
geneticAlgorithm.AddOperation(crossOverProbabality, new SpliceNoRepeat(cityCount / 3));
geneticAlgorithm.AddOperation(mutationProbabality, new MutateShuffle());
return(geneticAlgorithm);
}
catch (Exception e)
{
Console.WriteLine(e);
throw;
}
}
static void Main(string[] args)
{
try
{
Display("------------ Load City Coodinates");
var travellingSalesman = new TravellingSalesMan();
var cities = travellingSalesman.LoadCities(Config.CityCount, Config.MaxXCordinate, Config.MaxYCordinate);
// 1 create initial population
Display("\n------------ Create Initial Solution Population");
BasicPopulation population = travellingSalesman.CreateInitialPopulation(Config.PopulationSize, Config.CityCount);
// 2 create fitness function
Display("\n------------ Create Fitness function - sum of total diatance of cities within the chromosome - distance low --> better chormosome");
var tspFitness = new TravellingSalesManFitnessScore(cities);
// 3 create GA trainer
Display("\n------------ Create GA Trainer for iterations");
TrainEA geneticAlgorithm = travellingSalesman.CreateGA(Config.CityCount, population, tspFitness, Config.CrossOverProbabality, Config.MutationProbabality);
// 4 iterate and create off spring of new solutions
Display("\n------------ Run GA for iterations until good solutions found");
geneticAlgorithm = travellingSalesman.RunGA(geneticAlgorithm, Config.MaxNumIterationsSameSolution);
// 5 display GA results
Display("\n------------ Display Final solution after iterations");
travellingSalesman.DisplaySolution(geneticAlgorithm, cities);
Display("\n------------ Fin");
Console.ReadKey();
}
catch (Exception e)
{
Console.WriteLine(e);
throw;
}
}
