public override object Clone(CloneInfo info)
{
object res = Utils.Eval(info.StackFrame, Expression);
var list = res as IEnumerable;
var splice = new Splice(list);
return(splice);
}
private static int CalculerProximite(Splice mot, Splice dico, int max = Int32.MaxValue)
{
if (max < 0)
{
return(max);
}
if (dico.Length == 0)
{
return(Math.Min(max, mot.Length * 2));
}
if (mot.Length == 0)
{
return(Math.Min(max, dico.Length * 2));
}
if (mot[0] == dico[0])
{
return(CalculerProximite(mot.Substring(1), dico.Substring(1)));
}
int best = max;
var remplacement = 3 + CalculerProximite(mot.Substring(1), dico.Substring(1), best - 3);
if (remplacement < best)
{
best = remplacement;
}
var suppression1 = 2 + CalculerProximite(mot.Substring(1), dico, best - 2);
if (suppression1 < best)
{
best = suppression1;
}
var suppression2 = 2 + CalculerProximite(mot, dico.Substring(1), best - 2);
if (suppression2 < best)
{
best = suppression2;
}
if (dico.Length >= 2 && mot.Length >= 2)
{
if (dico[0] == mot[1] && dico[1] == mot[0])
{
var inversion = 3 + CalculerProximite(mot.Substring(2), dico.Substring(2), best - 3);
if (inversion < best)
{
best = inversion;
}
}
}
return(best);
}
/// <summary>
/// Demonstrate the crossover splice operator. Two offspring will be created by swapping three
/// segments of the parents (two cut points). Some genes may repeat.
/// </summary>
public static void Splice()
{
Console.WriteLine("Crossover Splice");
// Create a random number generator
IGenerateRandom rnd = new MersenneTwisterGenerateRandom();
// Create a new population.
IPopulation pop = new BasicPopulation();
pop.GenomeFactory = new IntegerArrayGenomeFactory(10);
// Create a trainer with a very simple score function. We do not care
// about the calculation of the score, as they will never be calculated.
IEvolutionaryAlgorithm train = new BasicEA(pop, new NullScore());
// Create a splice operator, length = 5. Use it 1.0 (100%) of the time.
var opp = new Splice(5);
train.AddOperation(1.0, opp);
// Create two parents, the genes are set to 1,2,3,4,5,7,8,9,10
// and 10,9,8,7,6,5,4,3,2,1.
var parents = new IntegerArrayGenome[2];
parents[0] = (IntegerArrayGenome)pop.GenomeFactory.Factor();
parents[1] = (IntegerArrayGenome)pop.GenomeFactory.Factor();
for (int i = 1; i <= 10; i++)
{
parents[0].Data[i - 1] = i;
parents[1].Data[i - 1] = 11 - i;
}
// Create an array to hold the offspring.
var offspring = new IntegerArrayGenome[2];
// Perform the operation
opp.PerformOperation(rnd, parents, 0, offspring, 0);
// Display the results
Console.WriteLine("Parent 1: " + string.Join(",", parents[0].Data));
Console.WriteLine("Parent 2: " + string.Join(",", parents[1].Data));
Console.WriteLine("Offspring 1: " + string.Join(",", offspring[0].Data));
Console.WriteLine("Offspring 2: " + string.Join(",",
offspring[1].Data));
}
public void TestGlue()
{
List <double> BListBBuf = new List <double>();
List <double> BListBFile = new List <double>();
List <double> Testi = new List <double>();
List <double> Testin = new List <double>();
BListBBuf.Add(1.1148);
BListBFile.Add(1.1146);
Splice Test = new Splice();
Testi = Test.glue(BListBFile, BListBBuf, 1, 1);
Testin.Add(1.1146);
Testin.Add(1.1148);
Assert.AreEqual(Testin[0], Testi[0]);
Assert.AreEqual(Testin[1], Testi[1]);
BListBBuf.Add(1.1142);
BListBFile.Add(1.1141);
Testi = Test.glue(BListBFile, BListBBuf, 2, 2);
List <double> Testin1 = new List <double>();
Testin1.Add(1.1146);
Testin1.Add(1.1141);
Testin1.Add(1.1148);
Testin1.Add(1.1142);
Assert.AreEqual(Testin1[0], Testi[0]);
Assert.AreEqual(Testin1[2], Testi[2]);
Assert.AreEqual(Testin1[3], Testi[3]);
Assert.AreEqual(4, Testi.Count);
Testi = Test.glue(BListBBuf, BListBFile);
Assert.AreEqual(3, Testi.Count);
Assert.AreEqual(Testin1[0], Testi[0]);
Assert.AreEqual(Testin1[3], Testi[2]);
}
//多组合并
public void Merge(Splice group)
{
_str.Append(group._str);
}
