/// <summary>
/// Get cross combined off-spring pair
/// </summary>
/// <returns></returns>
public OffSpringPair <int> GetCrossCombinedOffSpringPair(ChromosomeSegment segment, ParentPair <int> parentPair)
{
var parentA = GetChildCrossParent(parentPair.ParentB, parentPair.ParentA, segment);
var parentB = GetChildCrossParent(parentPair.ParentA, parentPair.ParentB, segment);
return(new OffSpringPair <int>(parentA, parentB));
}
protected virtual int[] GetChildCrossParent(int[] parentA, int[] parentB, ChromosomeSegment segment)
{
int[] child = new int[_number];
int segmentLengh = segment.SegmentIndexB - segment.SegmentIndexA + 1;
Array.Copy(parentA, segment.SegmentIndexA, child, segment.SegmentIndexA, segmentLengh);
var segmentChildATabuList = new HashSet <int>(child.Skip(segment.SegmentIndexA).Take(segmentLengh));
var outerSegmentParentATabuList = new HashSet <int>(parentB.OuterSegments(segment.SegmentIndexA, segment.SegmentIndexB));
Action <int> fillChild = (i) =>
{
if (!segmentChildATabuList.Contains(parentB[i]))
{
child[i] = parentB[i];
}
else
{
for (int indexFirst = 0; indexFirst < segment.SegmentIndexA; indexFirst++)
{
if (!outerSegmentParentATabuList.Contains(parentA[indexFirst]))
{
child[i] = parentA[indexFirst];
outerSegmentParentATabuList.Add(parentA[indexFirst]);
return;
}
}
for (int indexSecond = segment.SegmentIndexB + 1; indexSecond < _number; indexSecond++)
{
if (!outerSegmentParentATabuList.Contains(parentA[indexSecond]))
{
child[i] = parentA[indexSecond];
outerSegmentParentATabuList.Add(parentA[indexSecond]);
return;
}
}
}
};
for (int index = 0; index < segment.SegmentIndexA; index++)
{
fillChild(index);
}
for (int index = segment.SegmentIndexB + 1; index < _number; index++)
{
fillChild(index);
}
return(child);
}
public override void Start(CancellationToken token, Action <double> action)
{
_token = token;
if (_config.UseBigValleySearch)
{
OptimizerInfo.OnNext("Starting Genetic Optimizer with Big-Valley-Search");
}
else
{
OptimizerInfo.OnNext("Starting Genetic Optimizer without Big-Valley-Search");
}
_action = action;
double[] distances = new double[_population];
MinTour = Enumerable.Range(0, _number).ToArray();
int[,] chromosomePool = new int[_population, _number];
SetPopulationPool(chromosomePool, distances);
if (!token.IsCancellationRequested)
{
OptimizerInfo.OnNext("Starting genetic optimization");
}
while (!token.IsCancellationRequested)
{
// Forcing delay for visualization
if (_config.UseDelay)
{
Thread.Sleep(_config.DelayTime);
}
if (_random.NextDouble() < _rate)
{
int i, j, parent1, parent2;
int[] p1 = new int[_number];
int[] p2 = new int[_number];
i = _random.Next(_population);
j = _random.Next(_population);
if (distances[i] < distances[j])
{
parent1 = i;
}
else
{
parent1 = j;
}
i = _random.Next(_population);
j = _random.Next(_population);
if (distances[i] < distances[j])
{
parent2 = i;
}
else
{
parent2 = j;
}
for (i = 0; i < _number; i++)
{
p1[i] = chromosomePool[parent1, i];
p2[i] = chromosomePool[parent2, i];
}
int cp1 = -1, cp2 = -1;
do
{
cp1 = _random.Next(_number);
cp2 = _random.Next(_number);
} while (cp1 == cp2 || cp1 > cp2);
ChromosomeSegment segment = new ChromosomeSegment(cp1, cp2);
var offSpringPair = _partiallyMappedCrossover.GetCrossCombinedOffSpringPair(segment, new ParentPair <int>(p1, p2));
int[] o1 = offSpringPair.ChildA;
int[] o2 = offSpringPair.ChildB;
double o1Fitness = TourDistance(o1);
double o2Fitness = TourDistance(o2);
if (o1Fitness < distances[parent1])
{
for (i = 0; i < _number; i++)
{
chromosomePool[parent1, i] = o1[i];
}
}
if (o2Fitness < distances[parent2])
{
for (i = 0; i < _number; i++)
{
chromosomePool[parent2, i] = o2[i];
}
}
for (int p = 0; p < _population; p++)
{
if (distances[p] < MinDistance)
{
MinDistance = distances[p];
for (int n = 0; n < _number; n++)
{
MinTour[n] = chromosomePool[p, n];
}
}
}
}
else
{
int p;
int[] child = new int[_number];
int i = _random.Next(_population);
int j = _random.Next(_population);
if (distances[i] < distances[j])
{
p = i;
}
else
{
p = j;
}
for (int n = 0; n < _number; n++)
{
child[n] = chromosomePool[p, n];
}
child = DoMutation(child);
double childDistance = TourDistance(child);
int maxIndex = int.MaxValue;
double maxDistance = double.MinValue;
for (int q = 0; q < _population; q++)
{
if (distances[q] >= maxDistance)
{
maxIndex = q;
maxDistance = distances[q];
}
}
int[] index = new int[_population];
int count = 0;
for (int q = 0; q < _population; q++)
{
if (distances[q] == maxDistance)
{
index[count++] = q;
}
}
maxIndex = index[_random.Next(count)];
if (childDistance < distances[maxIndex])
{
distances[maxIndex] = childDistance;
for (int n = 0; n < _number; n++)
{
chromosomePool[maxIndex, n] = child[n];
}
if (childDistance < MinDistance)
{
MinDistance = childDistance;
for (int n = 0; n < _number; n++)
{
MinTour[n] = child[n];
}
}
}
}
}
_optimalSequence.OnCompleted();
OptimizerInfo.OnNext("Genetic optimization terminated with distance: " + MinDistance.ToString() + " LU");
}
