public async Task <Genome> FindPathAsync(Roteiro map, Genome seed = null)
{
if (Mutate == null || Crossover == null || Fitness == null || Selection == null)
{
throw new System.Exception("GA cant run without all operators");
}
var locals = map.Destinations.ToList();
locals.Add(map.Depot);
await routeService.Prepare(locals);
var rand = RandomSingleton.Instance;
var startNode = map.Depot;
Populations.Clear();
var popusize = PopulationSize;
if (seed != null)
{
Populations.Add(seed);
popusize--;
}
Populations.AddRange(
Enumerable.Range(0, popusize)
.Select(_ => Genome.Generator(map, Settings)));
await CalcFitness();
for (int i = 0; i < GenerationLimit; i++)
{
var newpopulations = new List <Genome>();
for (int j = 0; j < BestSolutionToPick; j++)
{
newpopulations.Add(Populations[j]);
}
while (newpopulations.Count < Populations.Count)
{
if (newpopulations.Any(e => e.Trucks.SelectMany(l => l.Locals).Count() != map.Destinations.Count))
{
throw new System.Exception();
}
// Selection
var(nodemom, nodedad) = Selection.SelectCouple(Populations);
// CrossOver
var sons = Crossover.Make(nodemom, nodedad);
// Mutation
sons = sons.Select(s => Mutate.Apply(s)).ToArray();
newpopulations.AddRange(sons);
}
Populations = newpopulations.ToList();
await CalcFitness();
Best = Populations.First();
}
return(Best);
}
public override bool Find(IMap map, IHeuristic heuristic)
{
if (Mutate == null || Crossover == null || Fitness == null || Selection == null)
{
throw new System.Exception("GA cant run without all operators");
}
if (Fitness.Heuristic == null)
{
Fitness.Heuristic = heuristic;
}
var Adaptation = new Adaptation(map);
var rand = RandomFactory.Rand;
var startNode = map.StartNode;
var endNode = map.EndNode;
for (int i = 0; i < PopulationSize; i++)
{
Populations.Add(new Genome(map));
}
CalcFitness();
var step = 0;
OnStart(BuildArgs(step, map));
for (int i = 0; i < GenerationLimit; i++)
{
var newpopulations = new List <IGenome>();
Populations = Populations.OrderBy(o => o.Fitness).ToList();
for (int j = 0; j < BestSolutionToPick; j++)
{
Populations[j].Fitness = Fitness.Calc(Populations[j]);
newpopulations.Add(Populations[j]);
}
var ran = rand.Next(1, Populations.Count);
var best = Populations.First().ListNodes;
var best2 = Selection.Select(Populations).ListNodes;
endNode = best.Last();
if (endNode.Equals(map.EndNode))
{
endNode.Direction = DirectionMovement.None;
map.EndNode = endNode;
map.UpdateClosedList(new List <Node>());
map.UpdateOpenList(new List <Node>());
//if (!best.First().Equals(map.StartNode))
OnEnd(BuildArgs(step, map, true));
Generations = i;
return(true);
}
map.UpdateClosedList(best);
map.UpdateOpenList(best2);
while (newpopulations.Count < Populations.Count)
{
// Selection
var nodemom = Selection.Select(Populations);
var nodedad = Selection.Select(Populations);
// CrossOver
var cross = Crossover.Calc(new CrossoverOperation(nodemom, nodedad));
//if (!cross.Mom.ListNodes.First().Equals(map.StartNode)) { }
//if (!cross.Dad.ListNodes.First().Equals(map.StartNode)) { }
// Mutation
nodemom = Mutate.Calc(cross.Mom);
nodedad = Mutate.Calc(cross.Dad);
//if (!nodemom.ListNodes.First().Equals(map.StartNode)) { }
//if (!nodedad.ListNodes.First().Equals(map.StartNode)) { }
// Adaptation
nodemom = Adaptation.Calc(nodemom);
nodedad = Adaptation.Calc(nodedad);
nodemom.Fitness = Fitness.Calc(nodemom);
nodedad.Fitness = Fitness.Calc(nodedad);
//if (!nodemom.ListNodes.First().Equals(map.StartNode)) { }
//if (!nodedad.ListNodes.First().Equals(map.StartNode)) { }
// Add in new population
newpopulations.Add(nodemom);
newpopulations.Add(nodedad);
}
Populations = newpopulations.ToList();
OnStep(BuildArgs(step++, map));
}
Generations = GenerationLimit;
OnEnd(BuildArgs(step, map, false));
return(false);
}