static void Main(String[] args)
{
//设置染色体组
//设置最优染色体
//设置子染色体组
//检查是否达到最优
//变异操作
//记录每次最优的情况
//按照适应度对种群的个体进行排序
/*
* func SortSuit()
* {
*
* }
*/
GA g = new GA();
g.initialization();
g.caculatefitness();
}
public void breed()
{
if (ga == null)
{
ga = new GA.GA <NEAT.Person>(player.weapon, new GA.Selection.NEAT_Selection(), new GA.Crossover.NEAT_Crossover(), new GA.Mutation.NEAT_Mutation(), new GA.Fitness.NEAT_Fitness(), 10, 0.05f, 0.1f);
}
else
{
ga.updatePopulation(player.usedWepons);
ga.updateParameters(ga.population_size, ga.elitesP, crossoverP);
}
ga.breed();
crossoverP += 0.01f;
if (crossoverP > 0.6f)
{
crossoverP = 0.6f;
}
player.usedWepons.Clear();
/*System.Random random = new System.Random();
* player.weapon = new List<NEAT.Person>() { ga.results[random.Next(ga.results.Count - 1)], ga.results[random.Next(ga.results.Count - 1)], ga.results[random.Next(ga.results.Count - 1)] };
* player.chosenW = player.weapon[0];
* player.chosenW.buildModel();*/
}
static void Main(string[] args)
{
//真实数据集
//string[] filepath = { "../Data/RealData/S1_500.txt", "../Data/RealData/S2_500.txt", "../Data/RealData/S3_500.txt", "../Data/RealData/S4_500.txt", "../Data/RealData/S5_500.txt" };//数据集规模500
string[] filepath = { "../Data/RealData/S1_400.txt", "../Data/RealData/S2_400.txt", "../Data/RealData/S3_400.txt", "../Data/RealData/S4_400.txt", "../Data/RealData/S5_400.txt" };//数据集规模400
//string[] filepath = { "../Data/RealData/S1_300.txt", "../Data/RealData/S2_300.txt", "../Data/RealData/S3_300.txt", "../Data/RealData/S4_300.txt", "../Data/RealData/S5_300.txt" };//数据集规模300
//string[] filepath = { "../Data/RealData/S1_200.txt", "../Data/RealData/S2_200.txt", "../Data/RealData/S3_200.txt", "../Data/RealData/S4_200.txt", "../Data/RealData/S5_200.txt" };//数据集规模200
//string[] filepath = { "../Data/RealData/S1_100.txt", "../Data/RealData/S2_100.txt", "../Data/RealData/S3_100.txt", "../Data/RealData/S4_100.txt", "../Data/RealData/S5_100.txt" };//数据集规模100
//随机数据集
//string[] filepath = { "../Data/Random/S1_500.txt", "../Data/Random/S2_500.txt", "../Data/Random/S3_500.txt", "../Data/Random/S4_500.txt", "../Data/Random/S5_500.txt" };//数据集规模500
//string[] filepath = { "../Data/Random/S1_400.txt", "../Data/Random/S2_400.txt", "../Data/Random/S3_400.txt", "../Data/Random/S4_400.txt", "../Data/Random/S5_400.txt" };//数据集规模400
//string[] filepath = { "../Data/Random/S1_300.txt", "../Data/Random/S2_300.txt", "../Data/Random/S3_300.txt", "../Data/Random/S4_300.txt", "../Data/Random/S5_300.txt" };//数据集规模300
//string[] filepath = { "../Data/Random/S1_200.txt", "../Data/Random/S2_200.txt", "../Data/Random/S3_200.txt", "../Data/Random/S4_200.txt", "../Data/Random/S5_200.txt" };//数据集规模200
//string[] filepath = { "../Data/Random/S1_100.txt", "../Data/Random/S2_100.txt", "../Data/Random/S3_100.txt", "../Data/Random/S4_100.txt", "../Data/Random/S5_100.txt" };//数据集规模100
GA_Server bestserver = new GA_Server();
List <GA_Server> scrlist = new List <GA_Server>();
List <Server>[] wlist = new List <Server> [ConstNum.PARTICE_DIM];
double fit = 0, totaltime = 0;
wlist = GetData.splitedatafromfile(filepath, ConstNum.PARTICE_DIM);
Timing dobj = new Timing();
int sum = 0;
double totalfit = 0;
double RMSE = 0;
double[] ttfit = new double[101];
for (int i = 0; i < 100; i++)
{
scrlist = GA_Server.GetinitGA_server(wlist);
dobj.startTime();
bestserver = GA.GetBest(scrlist, wlist, ref fit);
dobj.StopTime();
totaltime += dobj.Result().Milliseconds;
totaltime += dobj.Result().Seconds * 1000;
totalfit += fit;
ttfit[i] = fit;
if (Math.Abs(fit - 0.5608) <= 0.0005)//QWS--500,400
{
sum++;
}
//if (Math.Abs(fit - 0.6835) <= 0.0005)//QWS-300
//{
// sum++;
//}
//if (Math.Abs(fit - 0.7802) <= 0.0005)//QWS-200
//{
// sum++;
//}
//if (Math.Abs(fit - 0.8733) <= 0.0005)//QWS-100
//{
// sum++;
//}
//if (Math.Abs(fit - 1.4482) <= 0.0005)//RWS--500
//{
// sum++;
//}
//if (Math.Abs(fit - 1.4569) <= 0.0005)//RWS--400
//{
// sum++;
//}
//if (Math.Abs(fit - 1.4742) <= 0.0005)//RWS--300
//{
// sum++;
//}
//if (Math.Abs(fit - 1.5292) <= 0.0005)//RWS--200
//{
// sum++;
//}
//if (Math.Abs(fit - 1.5711) <= 0.0005)//RWS--100
//{
// sum++;
//}
Console.WriteLine("Best fit={0}", fit);
Console.WriteLine("The Best combination is {0},{1},{2},{3},{4}", bestserver.getIndextask(0), bestserver.getIndextask(1), bestserver.getIndextask(2), bestserver.getIndextask(3), bestserver.getIndextask(4));
Console.WriteLine("The time cost is {0}ms,{1}s,********{2}", dobj.Result().Milliseconds, dobj.Result().Seconds, sum);
}
//求均方根误差
for (int i = 0; i < 100; i++)
{
RMSE += (ttfit[i] - totalfit / 100) * (ttfit[i] - totalfit / 100);
}
RMSE = RMSE / 100;
RMSE = Math.Sqrt(RMSE);
//Console.WriteLine("Average is {0},time is {1},RMSE is {2},*******{3}", totalfit/ 100, totaltime / 100,RMSE,(double)sum/100);
Console.WriteLine("Average is {0}", totalfit / 100);
Console.WriteLine("time is {0}", totaltime / 100);
Console.WriteLine("RMSE is {0}", RMSE);
Console.WriteLine("*******{0}", (double)sum / 100);
Console.ReadKey();
}
static void Main(string[] args)
{
int numberOfGenerations = 50;
string[,] results = new string[numberOfGenerations, 7];
for (int i = 0; i < numberOfGenerations; i++)
{
Console.Clear();
Console.Write($"Number of Executions {i + 1}/{numberOfGenerations}");
GA ga = new GA();
ga.firstPop();
ga.population = ga.population.OrderByDescending(x => x.fitness).ToList();
int geracoesComResultadoIgual = 0;
double melhorFitness = 0;
for (int j = 0; j < ga.NUMBER_GENERATIONS; j++)
{
melhorFitness = ga.population.First().fitness;
ga.Crossover();
ga.Mutation();
ga.Elitism();
ga.NextGenerationSelection();
// Tratamento para retirar convergencia local
if (melhorFitness == ga.population.First().fitness)
{
geracoesComResultadoIgual++;
}
else
{
geracoesComResultadoIgual = 0;
}
if (geracoesComResultadoIgual == 6)
{
ga.firstPop();
geracoesComResultadoIgual = 0;
}
}
results[i, 0] = ga.POP_SIZE.ToString();
results[i, 1] = ga.NUMBER_GENERATIONS.ToString();
results[i, 2] = (100 * ga.MUTATION_RATE).ToString();
results[i, 3] = (100 * ga.REPRODUCTION_RATE).ToString();
results[i, 4] = ga.population.First().x.ToString("0.0000");
results[i, 5] = ga.population.First().y.ToString("0.0000");
results[i, 6] = ga.population.First().fitness.ToString("0.0000");
}
//Dá saída em um arquivo
var nomeArquivo = $"Results GA{DateTime.Now.ToString("yyyyMMdd-HHmmffff")}.txt";
var fileDirectory = System.AppDomain.CurrentDomain.BaseDirectory.ToString() + nomeArquivo;
if (!System.IO.File.Exists(fileDirectory))
{
using (System.IO.File.Create(fileDirectory)) { }
}
using (System.IO.StreamWriter file = new System.IO.StreamWriter(fileDirectory, true))
{
file.WriteLine("-------------------------- Results --------------------------\n\n");
file.WriteLine(String.Format("Fitness|Execução|POP Size|Number of Generations|Mutation Rate|Crossover Rate|X |Y"));
for (int i = 0; i < numberOfGenerations; i++)
{
file.WriteLine(String.Format("{7}|{0}|{1}|{2}|{3}|{4}|{5}|{6}",
(i + 1).ToString().PadRight(8),
results[i, 0].PadRight(20),
results[i, 1].PadRight(18),
(results[i, 2] + "%").PadRight(15),
(results[i, 3] + "%").PadRight(17),
results[i, 4].PadLeft(7),
results[i, 5].PadLeft(7),
results[i, 6].PadLeft(7)));
}
file.Write("\n\n------------------------------------------------------------------------");
}
System.Diagnostics.Process.Start(fileDirectory);
Console.ReadKey();
System.IO.File.Delete(fileDirectory);
}
// Start is called before the first frame update
void Start()
{
/*var watch = System.Diagnostics.Stopwatch.StartNew();
*
*
* a = (NEAT.Person)ScriptableObject.CreateInstance("NEAT.Person");
*
* NEAT.GENES.Node a1 = new NEAT.GENES.Node(NEAT.GENES.Node.NODE.IN, 0);
* NEAT.GENES.Node a2 = new NEAT.GENES.Node(NEAT.GENES.Node.NODE.IN, 1);
* NEAT.GENES.Node b1 = new NEAT.GENES.Node(NEAT.GENES.Node.NODE.HIDDEN, 2);
* NEAT.GENES.Node c1 = new NEAT.GENES.Node(NEAT.GENES.Node.NODE.HIDDEN, 3);
* NEAT.GENES.Node d1 = new NEAT.GENES.Node(NEAT.GENES.Node.NODE.OUT, 4);
* NEAT.GENES.Node d2 = new NEAT.GENES.Node(NEAT.GENES.Node.NODE.OUT, 5);
*
* NEAT.GENES.Connection a1b1 = new NEAT.GENES.Connection(a1.nb, b1.nb, 0.5f);
* NEAT.GENES.Connection a2b1 = new NEAT.GENES.Connection(a2.nb, b1.nb, 0.5f);
* NEAT.GENES.Connection b1c1 = new NEAT.GENES.Connection(b1.nb, c1.nb, 0.5f);
* NEAT.GENES.Connection c1d1 = new NEAT.GENES.Connection(c1.nb, d1.nb, 0.5f);
* NEAT.GENES.Connection a1d1 = new NEAT.GENES.Connection(a1.nb, d1.nb, 0.5f);
* NEAT.GENES.Connection c1d2 = new NEAT.GENES.Connection(c1.nb, d2.nb, 0.5f);
* NEAT.GENES.Connection b1d2 = new NEAT.GENES.Connection(b1.nb, d2.nb, 0.2f);
* a.node_gene.Add(a1);
* a.node_gene.Add(a2);
* a.node_gene.Add(b1);
* a.node_gene.Add(c1);
* a.node_gene.Add(d1);
* a.node_gene.Add(d2);
*
* a.node_connect.Add(a1b1);
* a.node_connect.Add(a2b1);
* a.node_connect.Add(b1c1);
* a.node_connect.Add(c1d1);
* a.node_connect.Add(a1d1);
* a.node_connect.Add(c1d2);
* a.node_connect.Add(b1d2);
*
* a.buildModel();
* watch.Stop();
* Debug.Log(watch.ElapsedMilliseconds);
*
* List<float> inputs = new List<float>();
* inputs.Add(0.5f);
* inputs.Add(0.5f);
* Debug.Log(a.network.evaluate(inputs)[0]);
* Debug.Log(a.network.evaluate(inputs)[1]);*/
//a.instantiate();
a.buildModel();
//b.instantiate();
b.buildModel();
c.buildModel();
foreach (NEAT.GENES.Connection connection in a.node_connect)
{
NEAT.GENES.Connection.addConnection(connection);
}
foreach (NEAT.GENES.Connection connection in b.node_connect)
{
NEAT.GENES.Connection.addConnection(connection);
}
foreach (NEAT.GENES.Connection connection in c.node_connect)
{
NEAT.GENES.Connection.addConnection(connection);
}
List <NEAT.Person> population = new List <NEAT.Person>();
population.Add(a);
population.Add(b);
population.Add(c);
Debug.Log(a);
Debug.Log(b);
Debug.Log(c);
NEAT.GENES.Connection.global_innov = 7;
ga = new GA.GA <NEAT.Person>(population, new GA.Selection.NEAT_Selection(), new GA.Crossover.NEAT_Crossover(), new GA.Mutation.NEAT_Mutation(), new GA.Fitness.NEAT_Fitness(), 10, 0.05f, 0.2f);
//ga.breed();
//for (int i = 0; i < ga.results.Count; i++)
// Debug.Log(ga.results[i]);
//a.network.randomizeWeight();
chosen = a;
}
