private void CrossOverParents(CandidateSolution parent1, CandidateSolution parent2,
out CandidateSolution child1, out CandidateSolution child2)
{
child1 = parent1.DeepClone();
child2 = parent2.DeepClone();
// Do we use exact copies of parents, or crossover?
if (Randomizer.GetDoubleFromZeroToOne() < crossoverRate)
{
// determine start and end crossover points
int numCities = parent1.NumCities - 1;
int start = Randomizer.IntLessThan(numCities - 2);
int remaining = numCities - start;
int end = Randomizer.IntBetween(numCities - remaining + 1, numCities - 1);
for (int i = start; i <= end; i++)
{
// the two values here tell us how to swap within each child
int child1cityIndex = child1.Path[i];
int child2cityIndex = child2.Path[i];
if (child1cityIndex == child2cityIndex)
{
continue;
}
// get references to both children's paths
var c1List = child1.Path.ToList();
var c2List = child2.Path.ToList();
// find within child1 and swap
int firstAt = c1List.IndexOf(child1cityIndex);
int secondAt = c1List.IndexOf(child2cityIndex);
int temp = c1List[firstAt];
c1List[firstAt] = c1List[secondAt];
c1List[secondAt] = temp;
// find within child2 and swap
firstAt = c2List.IndexOf(child1cityIndex);
secondAt = c2List.IndexOf(child2cityIndex);
temp = c2List[firstAt];
c2List[firstAt] = c2List[secondAt];
c2List[secondAt] = temp;
}
child1.CheckValidity();
child2.CheckValidity();
}
}
private CandidateSolution SelectCandidateViaTournament()
{
// pick N random candidates, and then return the one with the highest fitness
int bestFitness = int.MinValue;
CandidateSolution bestFound = null;
for (int i = 0; i < tourneySize; i++)
{
int randomIndex = Randomizer.IntLessThan(populationSize);
CandidateSolution randomSolution = currentGeneration[randomIndex];
int fitness = randomSolution.Fitness;
if (fitness > bestFitness)
{
bestFitness = fitness;
bestFound = randomSolution;
}
}
return(bestFound);
}
public PathChosen FindOptimalPath(CityCollection cities)
{
// create generation 0 with all randomly created possible solutions
currentGeneration = new List <CandidateSolution>(populationSize);
for (int i = 0; i < populationSize; i++)
{
currentGeneration.Add(new CandidateSolution(cities));
}
int generationNumber = 1;
while (true)
{
totalFitnessThisGeneration = 0;
int bestFitnessScoreThisGeneration = System.Int32.MinValue;
CandidateSolution bestSolutionThisGeneration = null;
foreach (var candidate in currentGeneration)
{
candidate.CalculatePathLength();
}
// find the longest path, which is the worst solution
int longestDistance = currentGeneration.Max(c => c.PathLength);
// then subtract each candidate's distance from that maximum distance
foreach (var candidate in currentGeneration)
{
// set fitness so the shortest path is the highest value, and the worst is 0
candidate.Fitness = longestDistance - candidate.PathLength;
// sum up the fitness scores for our roulette wheel selection
totalFitnessThisGeneration += candidate.Fitness;
if (candidate.Fitness > bestFitnessScoreThisGeneration)
{
bestFitnessScoreThisGeneration = candidate.Fitness;
bestSolutionThisGeneration = candidate;
}
}
// Ranked fitnesses for smoothing out selection when fitness scores vary widely
//currentGeneration = currentGeneration.OrderBy(g => g.Fitness).ToList();
//int ranking = 1;
//foreach (var candidate in currentGeneration)
//{
// candidate.Fitness = ranking;
// ranking++;
//}
int shortestPathThisGeneration = bestSolutionThisGeneration.PathLength;
// compare this generation's best to the best that have come before it
if (shortestPathThisGeneration < shortestPathAllTime)
{
// save the best score
shortestPathAllTime = shortestPathThisGeneration;
// and save the possible solution
bestSolution = bestSolutionThisGeneration.DeepClone();
bestSolutionGenerationNumber = generationNumber;
}
else
if ((generationNumber - bestSolutionGenerationNumber) >
NoChangeGenerationCountForTermination)
{
break;
}
// create the next generation
List <CandidateSolution> nextGeneration = new List <CandidateSolution>();
// Elitism
int numElitesToAdd = (int)(elitismRate * populationSize);
var theBest = currentGeneration.OrderBy(c => c.PathLength).Take(numElitesToAdd);
foreach (var peakPerformer in theBest)
{
nextGeneration.Add(peakPerformer);
}
while (nextGeneration.Count < populationSize)
{
CandidateSolution parent1, parent2;
// pick two parents based on good fitness
parent1 = SelectCandidate();
parent2 = SelectCandidate();
//parent1 = SelectCandidateViaTournament();
//parent2 = SelectCandidateViaTournament();
// cross them over to generate two new children
CandidateSolution child1, child2;
CrossOverParents(parent1, parent2, out child1, out child2);
// apply mutation to the children if needed
//child1.DoSwapMutation(mutationRate);
//child2.DoSwapMutation(mutationRate);
child1.DoDisplacementMutation(mutationRate);
child2.DoDisplacementMutation(mutationRate);
// and then add them to the next generation
nextGeneration.Add(child1);
nextGeneration.Add(child2);
}
// move to the next generation
currentGeneration = nextGeneration;
generationNumber++;
}
//Debug.WriteLine(shortestPathAllTime + " found at generation " + bestSolutionGenerationNumber);
return(bestSolution.Path);
}
