public TrainEA CreateGA(int powerUnitCount, BasicPopulation population, PowerUnitMaintainanceFitnessFunction maintainanceFitness, double crossOverProbabality, double mutationProbabality)
{
try
{
var geneticAlgorithm = new TrainEA(population, maintainanceFitness);
geneticAlgorithm.AddOperation(crossOverProbabality, new CustomCrossOver());
geneticAlgorithm.AddOperation(mutationProbabality, new CustomMutation());
return(geneticAlgorithm);
}
catch (Exception e)
{
Console.WriteLine(e);
throw;
}
}
static void Main(string[] args)
{
var powerUnitRepository = new PowerUnitRepository();
var powerUnitGALogic = new PowerUnitGALogic();
var populationSize = 500;
var crossOverProbabality = 0.9;
var mutationProbabaity = 0.001;
const int MaxNumIterationsSameSolution = 25; // maximum number of iterations which GA could run on a same error rate
Display("------------ Load Power Unit Details");
var powerUnits = powerUnitRepository.PowerUnits;
DisplayPowerUnitData(powerUnits);
// 1 create initial population
Display("------------ Create Initial Population");
var population = powerUnitGALogic.CreateInitialPopulation(populationSize, powerUnits.Count);
// 2 create fitness function
Display("\n------------ Create Fitness function - sum of total diatance of cities within the chromosome - distance low --> better chromosome");
double maxPossiblePower = powerUnits.Sum(x => x.UnitCapacity);
var numberOfIntervals = new IntervalFitnessDataRepository(maxPossiblePower).GetNumberOfIntervals();
var powerUnitMaintainanceFitness = new PowerUnitMaintainanceFitnessFunction(powerUnitRepository.GetAllPowerUnits(), numberOfIntervals, maxPossiblePower);
// 3 create GA trainer
Display("\n------------ Create GA Trainer for iterations");
TrainEA geneticAlgorithm = powerUnitGALogic.CreateGA(powerUnits.Count, population, powerUnitMaintainanceFitness, crossOverProbabality, mutationProbabaity);
// 4 iterate and create off spring of new solutions
Display("\n------------ Run GA for iterations until good solutions found");
geneticAlgorithm = powerUnitGALogic.RunGA(geneticAlgorithm, MaxNumIterationsSameSolution);
// 5 display GA results
Display("\n------------ Display Final solution after iterations");
powerUnitGALogic.DisplaySolution(geneticAlgorithm, powerUnitRepository.GetAllPowerUnits(), maxPossiblePower);
Display("\n------------ Done");
Console.ReadKey();
}