public bool TestSubstitutionEncode()
{
Substitution sub = new Substitution(plain: "the international collegiate programming contest is an annual competitive programming competition among the universities of the world");
sub.Key = sub.GenerateKey();
(var cipher, _) = sub.Encode();
Print(sub.Plain, sub.Key, cipher);
return(cipher == "WNIIR wniir");
}
public (string, double) Break(string cipher)
{
List <Individual> group = new List <Individual>();
sub.Cipher = cipher;
for (int i = 0; i < InitNum; i++)
{
group.Add(new Individual(sub.GenerateKey(), this));
}
int cnt = 0, T = 0;
double last = double.MinValue;
while (cnt < 5 && T < 15)
{
T++;
// 去重
HashSet <Individual> set = new HashSet <Individual>();
foreach (var id in group)
{
set.Add(id);
}
group.Clear();
foreach (var id in set)
{
group.Add(id);
}
set.Clear();
// 按照适应度降序排序
group.Sort();
group.Reverse();
// 破解中间结果
sub.ProcessLog.Enqueue(Decode(group[0].key));
// 控制个体数量
if (group.Count > Cap)
{
group = group.GetRange(0, Cap);
}
minFitness = group.Last().fitness;
// 最大适应度是否与上次相同，
if (group[0].fitness != last)
{
last = group[0].fitness;
cnt = 1;
}
else
{
cnt++;
}
// 计算浓度与基于适应度排名的线性概率
var concentrations = CalConcentration(group);
double[] fitnessProbs = new double[group.Count];
for (int i = 0; i < fitnessProbs.Length; i++)
{
fitnessProbs[i] = (double)(group.Count - i) / group.Count;
}
Random rand = new Random();
List <Individual> newGroup = new List <Individual>();
for (int i = 0; i < group.Count; i++)
{
// 选择
if (rand.NextDouble() <= Alpha * fitnessProbs[i] + (1 - Alpha) * concentrations[i])
{
var kids = CalMutation(group[i]);
newGroup.AddRange(kids);
}
}
group.AddRange(newGroup);
}
group.Sort();
group.Reverse();
sub.ProcessLog.Enqueue(Decode(group[0].key));
sub.ProcessLog.Enqueue("");
return(Decode(group[0].key), group[0].fitness);
}