public List <IIndividual> Generate()
{
List <IIndividual> pop = new List <IIndividual>();
for (int i = 0; i < PopSize; ++i)
{
GEPIndividual ind = new GEPIndividual(HeadLen, FunctionSet, ConstMinVal, ConstMaxVal, OptimizeGenerationCount);
ind.Optimize(Data);
pop.Add(ind);
}
return(pop);
}
public void Cross(IIndividual ind)
{
if (ind is GEPIndividual)
{
GEPIndividual gep = (GEPIndividual)ind;
int r1 = RandomUtil.U(0, gene.Count);
int r2 = RandomUtil.U(r1, gene.Count);
for (int i = r1; i <= r2; ++i)
{
Function t = gene[i];
gene[i] = gep.gene[i];
gep.gene[i] = t;
}
}
}
public object Clone()
{
GEPIndividual ind = new GEPIndividual();
for (int i = 0; i < gene.Count; ++i)
{
ind.gene.Add(gene[i]);
}
for (int i = 0; i < coeff.Count; ++i)
{
ind.coeff.Add(coeff[i]);
}
ind.functionSet = functionSet;
ind.optimizeGenerationCount = optimizeGenerationCount;
return(ind);
}
public List <IIndividual> Evolve(List <IIndividual> initPop)
{
int popSize = initPop.Count;
List <IIndividual> pop = initPop;
// 开始进化
int cnt = 0;
int iGen = 0;
while (true)
{
if (cnt > MaxCount)
{
break;
}
// 计算所有个体的误差 查找最优子代
List <double> errs = new List <double>();
IIndividual best = pop[0];
errs.Add(best.Error(Data));
double minErr = errs[0];
bool findBest = false;
for (int i = 1; i < popSize; ++i)
{
errs.Add(pop[i].Error(Data));
if (errs[i] < minErr)
{
best = pop[i];
minErr = errs[i];
findBest = true;
}
}
if (findBest)
{
cnt = 0;
}
else
{
cnt++;
}
// 输出最优个体
Console.WriteLine(iGen);
Console.WriteLine(best.ExprString);
Console.WriteLine("error: " + minErr);
Console.WriteLine();
// 备份最好个体
GEPIndividual bestBackup = (GEPIndividual)best.Clone();
// 锦标赛选择 变异
List <IIndividual> newPop = new List <IIndividual>();
for (int i = 0; i < popSize; i += 2)
{
if (errs[i] < errs[i + 1])
{
pop[i].Mutate();
pop[i].Optimize(Data);
newPop.Add(pop[i]);
}
else
{
pop[i + 1].Mutate();
pop[i + 1].Optimize(Data);
newPop.Add(pop[i + 1]);
}
}
// 交叉
for (int i = 0; i < popSize / 4; ++i)
{
int r1 = RandomUtil.U(0, popSize / 2);
int r2 = RandomUtil.U(0, popSize / 2);
GEPIndividual p1 = (GEPIndividual)newPop[r1].Clone();
GEPIndividual p2 = (GEPIndividual)newPop[r2].Clone();
p1.Cross(p2);
p1.Optimize(Data);
p2.Optimize(Data);
newPop.Add(p1);
newPop.Add(p2);
}
// 保留精英
newPop[0] = bestBackup;
pop = newPop;
iGen++;
}
return(pop);
}