private void GoGeneticAlgorithm(LocationGA startLocation, LocationGA[] destinations)
{
if (startLocation == null)
{
throw new ArgumentNullException("startLocation");
}
if (destinations == null)
{
throw new ArgumentNullException("destinations");
}
if (_populationCount < 2)
{
throw new ArgumentOutOfRangeException("populationCount");
}
if (_populationCount % 2 != 0)
{
throw new ArgumentException("The populationCount parameter must be an even value.", "populationCount");
}
_startLocation = startLocation;
destinations = (LocationGA[])destinations.Clone();
foreach (var destination in destinations)
{
if (destination == null)
{
throw new ArgumentException("The destinations array can't contain null values.", "destinations");
}
}
// This commented method uses a search of the kind "look for the nearest non visited LocationGA".
// This is rarely the shortest path, yet it is already a "somewhat good" path.
//destinations = _GetFakeShortest(destinations);
_populationWithDistances = new KeyValuePair <LocationGA[], double> [_populationCount];
// Create initial population.
for (int solutionIndex = 0; solutionIndex < _populationCount; solutionIndex++)
{
var newPossibleDestinations = (LocationGA[])destinations.Clone();
// Try commenting the next 2 lines of code while keeping the _GetFakeShortest active.
// If you avoid the algorithm from running and press reset, you will see that it always
// start with a path that seems "good" but is not the best.
for (int randomIndex = 0; randomIndex < newPossibleDestinations.Length; randomIndex++)
{
RandomProvider.FullyRandomizeLocations(newPossibleDestinations);
}
var distance = LocationGA.GetTotalDistance(startLocation, newPossibleDestinations);
var pair = new KeyValuePair <LocationGA[], double>(newPossibleDestinations, distance);
_populationWithDistances[solutionIndex] = pair;
}
Array.Sort(_populationWithDistances, _sortDelegate);
}