public override void RunGA( State Situation, Population P )
{
double NumerositySum = 0.0;
double TimeStampSum = 0.0;
foreach( Classifier C in this.CList )
{
NumerositySum += C.N;
TimeStampSum += C.Ts * C.N;
}
if( Configuration.T - TimeStampSum / NumerositySum > Configuration.Theta_GA )
{
foreach( Classifier C in this.CList )
{
C.Ts = Configuration.T;
}
Classifier Parent_1 = this.SelectOffspring();
Classifier Parent_2 = this.SelectOffspring();
Classifier Child_1 = new SigmaNormalClassifier( ( SigmaNormalClassifier )Parent_1 );
Classifier Child_2 = new SigmaNormalClassifier( ( SigmaNormalClassifier )Parent_2 );
///nakata added
Child_1.F /= Child_1.N;
Child_2.F /= Child_2.N;
//////////////
Child_1.N = Child_2.N = 1;
Child_1.Exp = Child_2.Exp = 0;
Child_1.St = Child_2.St = 0;
Child_1.M = Child_2.M = 0;
Child_1.S = Child_2.S = 0;
//Child_1.Epsilon_0 = Child_2.Epsilon_0 = Configuration.Epsilon_0;
SigmaNormalClassifier SNP1 = ( SigmaNormalClassifier )Parent_1;
SigmaNormalClassifier SNP2 = ( SigmaNormalClassifier )Parent_2;
SigmaNormalClassifier SNC1 = ( SigmaNormalClassifier )Child_1;
SigmaNormalClassifier SNC2 = ( SigmaNormalClassifier )Child_2;
// 交叉
if( Configuration.MT.NextDouble() < Configuration.Chai )
{
Child_1.Crossover( Child_2 );
Child_1.P = ( Parent_1.P + Parent_2.P ) / 2;
//Child_1.Epsilon = Parent_1.Epsilon + Parent_2.Epsilon;
Child_1.Epsilon = ( Parent_1.Epsilon + Parent_2.Epsilon ) / 2;
Child_1.F = ( Parent_1.F + Parent_2.F ) / 2;
//Child_1.M = ( Parent_1.M + Parent_2.M ) / 2;
//Child_1.S = ( Parent_1.S + Parent_2.S ) / 2;
Child_1.Epsilon_0 = ( Parent_1.Epsilon_0 + Parent_2.Epsilon_0 ) / 2;
//Child_1.Epsilon_0 = Math.Min(Parent_1.Epsilon_0, Parent_2.Epsilon_0);
Child_2.P = Child_1.P;
Child_2.Epsilon = Child_1.Epsilon;
Child_2.F = Child_1.F;
//Child_2.M = Child_1.M;
//Child_2.S = Child_1.S;
Child_2.Epsilon_0 = Child_1.Epsilon_0;
for( int i = 0; i < SNC1.WinningRate.Count(); i++ )
{
SNC1.WinningRate[i] = SNC2.WinningRate[i] = ( SNP1.WinningRate[i] + SNP2.WinningRate[i] ) / 2;
}
}
Child_1.F *= 0.1;
Child_2.F *= 0.1;
for( int i = 0; i < SNC1.WinningRate.Count(); i++ )
{
SNC1.WinningRate[i] *= 0.1;
SNC2.WinningRate[i] *= 0.1;
}
// bothChild
Child_1.Mutation( Situation );
Child_2.Mutation( Situation );
if( Configuration.DoGASubsumption )
{
if( Parent_1.DoesSubsume( Child_1 ) )
{
Parent_1.N++;
}
else if( Parent_2.DoesSubsume( Child_1 ) )
{
Parent_2.N++;
}
else
{
P.Insert( Child_1 );
}
P.Delete();
if( Parent_1.DoesSubsume( Child_2 ) )
{
Parent_1.N++;
}
else if( Parent_2.DoesSubsume( Child_2 ) )
{
Parent_2.N++;
}
else
{
P.Insert( Child_2 );
}
P.Delete();
}
else
{
P.Insert( Child_1 );
P.Delete();
P.Insert( Child_2 );
P.Delete();
}
}
}
public override void RunGA(State Situation, Population P)
{
double NumerositySum = 0.0;
double TimeStampSum = 0.0;
foreach (Classifier C in this.CList)
{
NumerositySum += C.N;
TimeStampSum += C.Ts * C.N;
}
if (Configuration.T - TimeStampSum / NumerositySum > Configuration.Theta_GA)
{
foreach (Classifier C in this.CList)
{
C.Ts = Configuration.T;
}
Classifier Parent_1 = this.SelectOffspring();
Classifier Parent_2 = this.SelectOffspring();
Classifier Child_1 = new NormalClassifier(( NormalClassifier )Parent_1);
Classifier Child_2 = new NormalClassifier(( NormalClassifier )Parent_2);
Child_1.N = Child_2.N = 1;
Child_1.Exp = Child_2.Exp = 0;
Child_1.St = Child_2.St = 0;
Child_1.M = Child_2.M = 0;
Child_1.S = Child_2.S = 0;
if (Configuration.MT.NextDouble() < Configuration.Chai)
{
Child_1.Crossover(Child_2);
Child_1.P = (Parent_1.P + Parent_2.P) / 2;
Child_1.Epsilon = (Parent_1.Epsilon + Parent_2.Epsilon) / 2;
Child_1.F = (Parent_1.F + Parent_2.F) / 2;
//Child_1.M = ( Parent_1.M + Parent_2.M ) / 2;
//Child_1.S = ( Parent_1.S + Parent_2.S ) / 2;
Child_2.P = Child_1.P;
Child_2.Epsilon = Child_1.Epsilon;
Child_2.F = Child_1.F;
//Child_2.M = Child_1.M;
//Child_2.S = Child_1.S;
}
Child_1.F *= 0.1;
Child_2.F *= 0.1;
// bothChild
Child_1.Mutation(Situation);
Child_2.Mutation(Situation);
if (Configuration.DoGASubsumption)
{
if (Parent_1.DoesSubsume(Child_1))
{
Parent_1.N++;
}
else if (Parent_2.DoesSubsume(Child_1))
{
Parent_2.N++;
}
else
{
P.Insert(Child_1);
}
P.Delete();
if (Parent_1.DoesSubsume(Child_2))
{
Parent_1.N++;
}
else if (Parent_2.DoesSubsume(Child_2))
{
Parent_2.N++;
}
else
{
P.Insert(Child_2);
}
P.Delete();
}
else
{
P.Insert(Child_1);
P.Delete();
P.Insert(Child_2);
P.Delete();
}
}
}
public override void RunGA(State Situation, Population P)
{
double NumerositySum = 0.0;
double TimeStampSum = 0.0;
foreach (Classifier C in this.CList)
{
NumerositySum += C.N;
TimeStampSum += C.Ts * C.N;
}
if (Configuration.T - TimeStampSum / NumerositySum > Configuration.Theta_GA)
{
foreach (Classifier C in this.CList)
{
C.Ts = Configuration.T;
}
Classifier Parent_1 = this.SelectOffspring();
Classifier Parent_2 = this.SelectOffspring();
Classifier Child_1 = new SigmaNormalClassifier(( SigmaNormalClassifier )Parent_1);
Classifier Child_2 = new SigmaNormalClassifier(( SigmaNormalClassifier )Parent_2);
///nakata added
Child_1.F /= Child_1.N;
Child_2.F /= Child_2.N;
//////////////
Child_1.N = Child_2.N = 1;
Child_1.Exp = Child_2.Exp = 0;
Child_1.St = Child_2.St = 0;
Child_1.M = Child_2.M = 0;
Child_1.S = Child_2.S = 0;
//Child_1.Epsilon_0 = Child_2.Epsilon_0 = Configuration.Epsilon_0;
SigmaNormalClassifier SNP1 = ( SigmaNormalClassifier )Parent_1;
SigmaNormalClassifier SNP2 = ( SigmaNormalClassifier )Parent_2;
SigmaNormalClassifier SNC1 = ( SigmaNormalClassifier )Child_1;
SigmaNormalClassifier SNC2 = ( SigmaNormalClassifier )Child_2;
// 交叉
if (Configuration.MT.NextDouble() < Configuration.Chai)
{
Child_1.Crossover(Child_2);
Child_1.P = (Parent_1.P + Parent_2.P) / 2;
//Child_1.Epsilon = Parent_1.Epsilon + Parent_2.Epsilon;
Child_1.Epsilon = (Parent_1.Epsilon + Parent_2.Epsilon) / 2;
Child_1.F = (Parent_1.F + Parent_2.F) / 2;
Child_1.Epsilon_0 = (Parent_1.Epsilon_0 + Parent_2.Epsilon_0) / 2;
//Child_1.Epsilon_0 = Math.Min(Parent_1.Epsilon_0, Parent_2.Epsilon_0);
Child_2.P = Child_1.P;
Child_2.Epsilon = Child_1.Epsilon;
Child_2.F = Child_1.F;
Child_2.Epsilon_0 = Child_1.Epsilon_0;
for (int i = 0; i < SNC1.WinningRate.Count(); i++)
{
SNC1.WinningRate[i] = SNC2.WinningRate[i] = (SNP1.WinningRate[i] + SNP2.WinningRate[i]) / 2;
}
}
Child_1.F *= 0.1;
Child_2.F *= 0.1;
for (int i = 0; i < SNC1.WinningRate.Count(); i++)
{
SNC1.WinningRate[i] *= 0.1;
SNC2.WinningRate[i] *= 0.1;
}
// bothChild
Child_1.Mutation(Situation);
Child_2.Mutation(Situation);
if (Child_1.C.state[4] == '0' & Child_1.C.state[7] == '1')//"bath0 rehabi1"
{
Configuration.Problem.WriteLine(Child_1.C.state + "," + Configuration.T + "," + Child_1.P + "," + Child_1.M + "," + Child_1.Epsilon + "," + Child_1.F + ","
+ Child_1.N + "," + Child_1.Exp + "," + Child_1.Ts + "," + Child_1.As + "," + Child_1.Kappa + "," + Child_1.Epsilon_0 + "," + Child_1.St + "," + Child_1.GenerateTime + ", child 2 ");
Configuration.Problem.WriteLine(Parent_1.C.state + "," + Configuration.T + "," + Parent_1.P + "," + Parent_1.M + "," + Parent_1.Epsilon + "," + Parent_1.F + ","
+ Parent_1.N + "," + Parent_1.Exp + "," + Parent_1.Ts + "," + Parent_1.As + "," + Parent_1.Kappa + "," + Parent_1.Epsilon_0 + "," + Parent_1.St + "," + Parent_1.GenerateTime + ",Parent1");
Configuration.Problem.WriteLine(Parent_2.C.state + "," + Configuration.T + "," + Parent_2.P + "," + Parent_2.M + "," + Parent_2.Epsilon + "," + Parent_2.F + ","
+ Parent_2.N + "," + Parent_2.Exp + "," + Parent_2.Ts + "," + Parent_2.As + "," + Parent_2.Kappa + "," + Parent_2.Epsilon_0 + "," + Parent_2.St + "," + Parent_2.GenerateTime + ",Parent2");
}
if (Child_2.C.state[4] == '0' & Child_2.C.state[7] == '1')//"bath0 rehabi1"
{
Configuration.Problem.WriteLine(Child_2.C.state + "," + Configuration.T + "," + Child_2.P + "," + Child_2.M + "," + Child_2.Epsilon + "," + Child_2.F + ","
+ Child_2.N + "," + Child_2.Exp + "," + Child_2.Ts + "," + Child_2.As + "," + Child_2.Kappa + "," + Child_2.Epsilon_0 + "," + Child_2.St + "," + Child_2.GenerateTime + ",child 2");
Configuration.Problem.WriteLine(Parent_1.C.state + "," + Configuration.T + "," + Parent_1.P + "," + Parent_1.M + "," + Parent_1.Epsilon + "," + Parent_1.F + ","
+ Parent_1.N + "," + Parent_1.Exp + "," + Parent_1.Ts + "," + Parent_1.As + "," + Parent_1.Kappa + "," + Parent_1.Epsilon_0 + "," + Parent_1.St + "," + Parent_1.GenerateTime + ",Parent1");
Configuration.Problem.WriteLine(Parent_2.C.state + "," + Configuration.T + "," + Parent_2.P + "," + Parent_2.M + "," + Parent_2.Epsilon + "," + Parent_2.F + ","
+ Parent_2.N + "," + Parent_2.Exp + "," + Parent_2.Ts + "," + Parent_2.As + "," + Parent_2.Kappa + "," + Parent_2.Epsilon_0 + "," + Parent_2.St + "," + Parent_2.GenerateTime + ",Parent2");
}
if (Configuration.DoGASubsumption)
{
if (Parent_1.DoesSubsume(Child_1))
{
Parent_1.N++;
}
else if (Parent_2.DoesSubsume(Child_1))
{
Parent_2.N++;
}
else
{
P.Insert(Child_1);
}
P.Delete();
if (Parent_1.DoesSubsume(Child_2))
{
Parent_1.N++;
}
else if (Parent_2.DoesSubsume(Child_2))
{
Parent_2.N++;
}
else
{
P.Insert(Child_2);
}
P.Delete();
}
else
{
P.Insert(Child_1);
P.Delete();
P.Insert(Child_2);
P.Delete();
}
}
}
public override void RunGA( State Situation, Population P )
{
double NumerositySum = 0.0;
double TimeStampSum = 0.0;
foreach( Classifier C in this.CList )
{
NumerositySum += C.N;
TimeStampSum += C.Ts * C.N;
}
if( Configuration.T - TimeStampSum / NumerositySum > Configuration.Theta_GA )
{
foreach( Classifier C in this.CList )
{
C.Ts = Configuration.T;
}
Classifier Parent_1 = this.SelectOffspring();
Classifier Parent_2 = this.SelectOffspring();
Classifier Child_1 = new NormalClassifier( ( NormalClassifier )Parent_1 );
Classifier Child_2 = new NormalClassifier( ( NormalClassifier )Parent_2 );
Child_1.N = Child_2.N = 1;
Child_1.Exp = Child_2.Exp = 0;
Child_1.St = Child_2.St = 0;
Child_1.M = Child_2.M = 0;
Child_1.S = Child_2.S = 0;
if( Configuration.MT.NextDouble() < Configuration.Chai )
{
Child_1.Crossover( Child_2 );
Child_1.P = ( Parent_1.P + Parent_2.P ) / 2;
Child_1.Epsilon = ( Parent_1.Epsilon + Parent_2.Epsilon ) / 2;
Child_1.F = ( Parent_1.F + Parent_2.F ) / 2;
//Child_1.M = ( Parent_1.M + Parent_2.M ) / 2;
//Child_1.S = ( Parent_1.S + Parent_2.S ) / 2;
Child_2.P = Child_1.P;
Child_2.Epsilon = Child_1.Epsilon;
Child_2.F = Child_1.F;
//Child_2.M = Child_1.M;
//Child_2.S = Child_1.S;
}
Child_1.F *= 0.1;
Child_2.F *= 0.1;
// bothChild
Child_1.Mutation( Situation );
Child_2.Mutation( Situation );
if( Configuration.DoGASubsumption )
{
if( Parent_1.DoesSubsume( Child_1 ) )
{
Parent_1.N++;
}
else if( Parent_2.DoesSubsume( Child_1 ) )
{
Parent_2.N++;
}
else
{
P.Insert( Child_1 );
}
P.Delete();
if( Parent_1.DoesSubsume( Child_2 ) )
{
Parent_1.N++;
}
else if( Parent_2.DoesSubsume( Child_2 ) )
{
Parent_2.N++;
}
else
{
P.Insert( Child_2 );
}
P.Delete();
}
else
{
P.Insert( Child_1 );
P.Delete();
P.Insert( Child_2 );
P.Delete();
}
}
}