Mutant UCB

Mutant UCB#

[3]:

import warnings
warnings.filterwarnings('ignore')
import torch
import torch.nn as nn
from torch.utils.data import Dataset, DataLoader
import openml
from sklearn.model_selection import train_test_split
import numpy as np
import os
from dragon.search_space.bricks_variables import mlp_var, identity_var, operations_var, mlp_const_var, dag_var, node_var
from dragon.search_space.cells import AdjMatrix, Node
from dragon.search_space.zellij_variables import ArrayVar
from dragon.search_algorithm.zellij_neighborhoods import ArrayInterval


dataset = openml.datasets.get_dataset(32)
data, _, numerical, names = dataset.get_data()
X = data.drop('class', axis=1)
y = data[["class"]].astype(int)
X_train, X_val, y_train, y_val = train_test_split(X, y, test_size=0.3, random_state=0)
class CustomDataset(Dataset):
    def __init__(self, X, y):
        super().__init__()
        self.X = torch.FloatTensor(X.values)
        self.y = torch.LongTensor(y.values)
    def __len__(self):
        return self.X.shape[0]
    def __getitem__(self, index):
        return self.X[index], self.y[index]
train_set = CustomDataset(X_train, y_train)
val_set = CustomDataset(X_val, y_val)
train_loader = DataLoader(train_set, batch_size=32, shuffle=True)
val_loader = DataLoader(val_set, batch_size=1, shuffle=False)

class MetaArchi(nn.Module):
    def __init__(self, args, input_shape):
        super().__init__()
        # Number of features, here equals to 16
        self.input_shape = input_shape

        # We create the DAG using the WeightsAdjCell module
        assert isinstance(args['Dag'], AdjMatrix), f"The 'Dag' argument should be an 'AdjMatrix'. Got {type(args['Dag'])} instead."
        self.dag = args['Dag']
        self.dag.set(input_shape)

        # We set the final layer
        assert isinstance(args['Out'], Node), f"The 'Out' argument should be a 'Node'. Got {type(args['Node'])} instead."
        self.output = args["Out"]
        self.output.set(self.dag.output_shape)

    def forward(self, X):
        out = self.dag(X)
        return self.output(out)

    def save(self, path):
        if not os.path.exists(path):
            os.makedirs(path)
        full_path = os.path.join(path, "best_model.pth")
        torch.save(self.state_dict(), full_path)


candidate_operations = operations_var("Candidate operations", size=10, candidates=[mlp_var("MLP"), identity_var("Identity")])
dag = dag_var("Dag", candidate_operations)
out = node_var("Out", operation=mlp_const_var('Operation', 10), activation_function=nn.Softmax())


search_space = ArrayVar(dag, out, label="Search Space", neighbor=ArrayInterval())

def train_model(model, data_loader, n_epochs=2):
    loss_fn = nn.CrossEntropyLoss()
    optimizer = torch.optim.SGD(model.parameters(), lr=0.05)
    model.train()
    for _ in range(n_epochs):
        for X,y in data_loader:
            optimizer.zero_grad()
            y = y.squeeze()
            pred = model(X)
            loss = loss_fn(pred,y)
            loss.backward()
            optimizer.step()
    return model

def test_model(model, data_loader):
    loss_fn = nn.CrossEntropyLoss()
    model.eval()
    test_loss, correct = 0, 0
    with torch.no_grad():
      for X, y in data_loader:
          y = y.squeeze(1)
          pred = model(X)
          loss = loss_fn(pred, y).item()
          test_loss += loss
          prediction = pred.argmax(axis=1)
          correct += (prediction == y).sum().item()
    accuracy = correct/ len(data_loader.dataset)
    return accuracy

def loss_function(args, idx, *kwargs):
    labels = [e.label for e in search_space]
    args = dict(zip(labels, args))
    model = MetaArchi(args, input_shape=(16,))
    model = train_model(model, train_loader)
    accuracy = test_model(model, val_loader)
    print(f'Idx = {idx}, accuracy = {accuracy}')
    return 1 - accuracy, model

2024-09-27 16:47:02,234 | INFO | pickle write pendigits

[4]:

from dragon.search_algorithm.mutant_ucb import Mutant_UCB


search_algorithm = Mutant_UCB(search_space, "test_mutant", T=500, N=5, K=10, E=0.01, evaluation=loss_function)
min_loss = search_algorithm.run()

2024-09-27 16:47:14,812 | WARNING | Install mpi4py if you want to use the distributed version.
Idx = 0, accuracy = 0.09702850212249849
Idx = 1, accuracy = 0.5576106731352335
Idx = 2, accuracy = 0.10430563978168587
Idx = 3, accuracy = 0.0585203153426319
Idx = 4, accuracy = 0.9035779260157671
Idx = 5, accuracy = 0.30442692540933897
Idx = 6, accuracy = 0.09945421467556094
Idx = 7, accuracy = 0.10430563978168587
Idx = 8, accuracy = 0.0979381443298969
Idx = 9, accuracy = 0.09702850212249849
2024-09-27 16:47:55,412 | INFO | With p = 0.2 = 1 / 5, training 4 instead
Idx = 4, accuracy = 0.8583990297149787
2024-09-27 16:48:00,996 | INFO | Best found! 0.14160097028502128 < inf
2024-09-27 16:48:00,999 | INFO | With p = 0.4 = 2 / 5, mutating 4 to 10
Idx = 10, accuracy = 0.9423893268647665
2024-09-27 16:48:06,641 | INFO | Best found! 0.05761067313523349 < 0.14160097028502128
2024-09-27 16:48:06,644 | INFO | With p = 0.2 = 1 / 5, training 10 instead
Idx = 10, accuracy = 0.9460278956943602
2024-09-27 16:48:12,228 | INFO | Best found! 0.05397210430563981 < 0.05761067313523349
2024-09-27 16:48:12,231 | INFO | With p = 0.4 = 2 / 5, training 10 instead
Idx = 10, accuracy = 0.9490600363856883
2024-09-27 16:48:17,818 | INFO | Best found! 0.050939963614311745 < 0.05397210430563981
2024-09-27 16:48:17,821 | INFO | With p = 0.6 = 3 / 5, mutating 10 to 11
Idx = 11, accuracy = 0.0979381443298969
2024-09-27 16:48:25,403 | INFO | With p = 0.6 = 3 / 5, mutating 10 to 12
Idx = 12, accuracy = 0.0979381443298969
2024-09-27 16:48:32,822 | INFO | With p = 0.6 = 3 / 5, mutating 10 to 13
Idx = 13, accuracy = 0.0979381443298969
2024-09-27 16:48:40,144 | INFO | With p = 0.6 = 3 / 5, mutating 10 to 14
Idx = 14, accuracy = 0.0979381443298969
2024-09-27 16:48:47,575 | INFO | With p = 0.6 = 3 / 5, mutating 10 to 15
Idx = 15, accuracy = 0.0979381443298969
2024-09-27 16:48:55,111 | INFO | With p = 0.6 = 3 / 5, training 10 instead
Idx = 10, accuracy = 0.0979381443298969
2024-09-27 16:49:02,630 | INFO | With p = 0.4 = 2 / 5, training 4 instead
Idx = 4, accuracy = 0.0979381443298969
2024-09-27 16:49:11,057 | INFO | With p = 0.8 = 4 / 5, mutating 10 to 16
Idx = 16, accuracy = 0.0979381443298969
2024-09-27 16:49:20,622 | INFO | With p = 0.8 = 4 / 5, mutating 10 to 17
Idx = 17, accuracy = 0.0979381443298969
2024-09-27 16:49:28,130 | INFO | With p = 0.8 = 4 / 5, mutating 10 to 18
Idx = 18, accuracy = 0.0939963614311704
2024-09-27 16:49:33,854 | INFO | With p = 0.8 = 4 / 5, mutating 10 to 19
Idx = 19, accuracy = 0.11218920557913888
2024-09-27 16:49:38,913 | INFO | With p = 0.8 = 4 / 5, mutating 10 to 20
Idx = 20, accuracy = 0.11249241964827168
2024-09-27 16:49:43,945 | INFO | With p = 0.8 = 4 / 5, mutating 10 to 21
Idx = 21, accuracy = 0.09702850212249849
2024-09-27 16:49:49,736 | INFO | With p = 0.8 = 4 / 5, mutating 10 to 22
Idx = 22, accuracy = 0.09702850212249849
2024-09-27 16:49:56,233 | INFO | With p = 0.8 = 4 / 5, training 10 instead
Idx = 10, accuracy = 0.09702850212249849
2024-09-27 16:50:02,761 | INFO | With p = 0.6 = 3 / 5, training 4 instead
Idx = 4, accuracy = 0.09702850212249849
2024-09-27 16:50:09,254 | INFO | With p = 0.2 = 1 / 5, training 1 instead
Idx = 1, accuracy = 0.6682838083687083
2024-09-27 16:50:12,658 | INFO | With p = 0.4 = 2 / 5, mutating 1 to 23
Idx = 23, accuracy = 0.6976955730745906
2024-09-27 16:50:15,313 | INFO | With p = 0.2 = 1 / 5, training 23 instead
Idx = 23, accuracy = 0.7007277137659187
2024-09-27 16:50:17,899 | INFO | With p = 0.4 = 2 / 5, mutating 23 to 24
Idx = 24, accuracy = 0.15463917525773196
2024-09-27 16:50:20,754 | INFO | With p = 0.4 = 2 / 5, mutating 23 to 25
Idx = 25, accuracy = 0.15463917525773196
2024-09-27 16:50:23,605 | INFO | With p = 0.4 = 2 / 5, training 23 instead
Idx = 23, accuracy = 0.15463917525773196
2024-09-27 16:50:26,453 | INFO | With p = 0.4 = 2 / 5, mutating 1 to 26
Idx = 26, accuracy = 0.6625227410551849
2024-09-27 16:50:32,026 | INFO | With p = 0.2 = 1 / 5, training 26 instead
Idx = 26, accuracy = 0.7701637355973318
2024-09-27 16:50:37,608 | INFO | With p = 0.4 = 2 / 5, mutating 26 to 27
Idx = 27, accuracy = 0.1052152819890843
2024-09-27 16:50:42,052 | INFO | With p = 0.4 = 2 / 5, training 26 instead
Idx = 26, accuracy = 0.1052152819890843
2024-09-27 16:50:46,370 | INFO | With p = 0.4 = 2 / 5, training 1 instead
Idx = 1, accuracy = 0.1052152819890843
2024-09-27 16:50:50,731 | INFO | With p = 1.0 = 5 / 5, mutating 10 to 28
Idx = 28, accuracy = 0.20436628259551243
2024-09-27 16:50:54,264 | INFO | With p = 1.0 = 5 / 5, mutating 10 to 29
Idx = 29, accuracy = 0.3086719223771983
2024-09-27 16:50:58,097 | INFO | With p = 1.0 = 5 / 5, mutating 10 to 30
Idx = 30, accuracy = 0.1052152819890843
2024-09-27 16:51:05,235 | INFO | With p = 1.0 = 5 / 5, mutating 10 to 31
Idx = 31, accuracy = 0.25833838690115224
2024-09-27 16:51:09,721 | INFO | With p = 1.0 = 5 / 5, mutating 10 to 32
Idx = 32, accuracy = 0.17283201940570042
2024-09-27 16:51:14,922 | INFO | With p = 1.0 = 5 / 5, mutating 10 to 33
Idx = 33, accuracy = 0.17283201940570042
2024-09-27 16:51:20,125 | INFO | With p = 1.0 = 5 / 5, mutating 10 to 34
Idx = 34, accuracy = 0.1052152819890843
2024-09-27 16:51:26,236 | INFO | With p = 1.0 = 5 / 5, mutating 10 to 35
Idx = 35, accuracy = 0.1052152819890843
2024-09-27 16:51:32,310 | INFO | With p = 1.0 = 5 / 5, mutating 10 to 36
Idx = 36, accuracy = 0.2189205579138872
2024-09-27 16:51:37,820 | INFO | With p = 1.0 = 5 / 5, mutating 10 to 37
Idx = 37, accuracy = 0.11613098847786538
2024-09-27 16:51:41,603 | INFO | With p = 1.0 = 5 / 5, mutating 10 to 38
Idx = 38, accuracy = 0.25257731958762886
2024-09-27 16:51:45,350 | INFO | With p = 1.0 = 5 / 5, mutating 10 to 39
Idx = 39, accuracy = 0.33020012128562765
2024-09-27 16:51:49,079 | INFO | With p = 1.0 = 5 / 5, mutating 10 to 40
Idx = 40, accuracy = 0.10491206791995149
2024-09-27 16:51:54,898 | INFO | With p = 1.0 = 5 / 5, mutating 10 to 41
Idx = 41, accuracy = 0.10491206791995149
2024-09-27 16:52:00,493 | INFO | With p = 1.0 = 5 / 5, mutating 10 to 42
Idx = 42, accuracy = 0.10491206791995149
2024-09-27 16:52:06,004 | INFO | With p = 1.0 = 5 / 5, mutating 10 to 43
Idx = 43, accuracy = 0.09945421467556094
2024-09-27 16:52:12,134 | INFO | With p = 1.0 = 5 / 5, mutating 10 to 44
Idx = 44, accuracy = 0.09945421467556094
2024-09-27 16:52:18,004 | INFO | With p = 1.0 = 5 / 5, mutating 10 to 45
Idx = 45, accuracy = 0.09945421467556094
2024-09-27 16:52:23,753 | INFO | With p = 1.0 = 5 / 5, mutating 10 to 46
Idx = 46, accuracy = 0.7252880533656761
2024-09-27 16:52:28,723 | INFO | With p = 0.2 = 1 / 5, training 46 instead
Idx = 46, accuracy = 0.9117647058823529
2024-09-27 16:52:33,678 | INFO | With p = 0.4 = 2 / 5, training 46 instead
Idx = 46, accuracy = 0.9226804123711341
2024-09-27 16:52:38,661 | INFO | With p = 0.6 = 3 / 5, mutating 46 to 47
Idx = 47, accuracy = 0.9329896907216495
2024-09-27 16:52:43,654 | INFO | With p = 0.2 = 1 / 5, training 47 instead
Idx = 47, accuracy = 0.9372346876895088
2024-09-27 16:52:48,639 | INFO | With p = 0.4 = 2 / 5, mutating 47 to 48
Idx = 48, accuracy = 0.9248029108550637
2024-09-27 16:52:54,035 | INFO | With p = 0.2 = 1 / 5, training 48 instead
Idx = 48, accuracy = 0.9311704063068527
2024-09-27 16:52:59,360 | INFO | With p = 0.4 = 2 / 5, training 48 instead
Idx = 48, accuracy = 0.9372346876895088
2024-09-27 16:53:04,711 | INFO | With p = 0.4 = 2 / 5, mutating 47 to 49
Idx = 49, accuracy = 0.9390539721043056
2024-09-27 16:53:10,077 | INFO | With p = 0.2 = 1 / 5, training 49 instead
Idx = 49, accuracy = 0.9417828987265009
2024-09-27 16:53:15,466 | INFO | With p = 0.4 = 2 / 5, training 49 instead
Idx = 49, accuracy = 0.9442086112795633
2024-09-27 16:53:20,792 | INFO | With p = 0.6 = 3 / 5, training 49 instead
Idx = 49, accuracy = 0.9439053972104305
2024-09-27 16:53:26,187 | INFO | With p = 0.8 = 4 / 5, mutating 49 to 50
Idx = 50, accuracy = 0.9496664645239539
2024-09-27 16:53:31,514 | INFO | Best found! 0.05033353547604613 < 0.050939963614311745
2024-09-27 16:53:31,518 | INFO | With p = 0.2 = 1 / 5, training 50 instead
Idx = 50, accuracy = 0.9493632504548211
2024-09-27 16:53:36,890 | INFO | With p = 0.4 = 2 / 5, training 50 instead
Idx = 50, accuracy = 0.9539114614918133
2024-09-27 16:53:42,189 | INFO | Best found! 0.04608853850818673 < 0.05033353547604613
2024-09-27 16:53:42,193 | INFO | With p = 0.6 = 3 / 5, training 50 instead
Idx = 50, accuracy = 0.9530018192844147
2024-09-27 16:53:47,541 | INFO | With p = 0.8 = 4 / 5, mutating 50 to 51
Idx = 51, accuracy = 0.9530018192844147
2024-09-27 16:53:52,867 | INFO | With p = 0.2 = 1 / 5, training 51 instead
Idx = 51, accuracy = 0.947847180109157
2024-09-27 16:53:58,204 | INFO | With p = 0.4 = 2 / 5, training 51 instead
Idx = 51, accuracy = 0.9520921770770163
2024-09-27 16:54:03,555 | INFO | With p = 0.6 = 3 / 5, training 51 instead
Idx = 51, accuracy = 0.9526986052152819
2024-09-27 16:54:08,882 | INFO | With p = 0.8 = 4 / 5, mutating 51 to 52
Idx = 52, accuracy = 0.10491206791995149
2024-09-27 16:54:13,240 | INFO | With p = 0.8 = 4 / 5, training 50 instead
Idx = 50, accuracy = 0.10491206791995149
2024-09-27 16:54:17,599 | INFO | With p = 0.8 = 4 / 5, mutating 51 to 53
Idx = 53, accuracy = 0.1052152819890843
2024-09-27 16:54:21,537 | INFO | With p = 0.8 = 4 / 5, training 51 instead
Idx = 51, accuracy = 0.10491206791995149
2024-09-27 16:54:25,471 | INFO | With p = 0.6 = 3 / 5, training 48 instead
Idx = 48, accuracy = 0.10278956943602183
2024-09-27 16:54:29,178 | INFO | With p = 0.8 = 4 / 5, mutating 49 to 54
Idx = 54, accuracy = 0.10278956943602183
2024-09-27 16:54:32,855 | INFO | With p = 0.4 = 2 / 5, training 47 instead
Idx = 47, accuracy = 0.10278956943602183
2024-09-27 16:54:36,544 | INFO | With p = 0.8 = 4 / 5, mutating 49 to 55
Idx = 55, accuracy = 0.10278956943602183
2024-09-27 16:54:40,222 | INFO | With p = 0.8 = 4 / 5, training 49 instead
Idx = 49, accuracy = 0.10278956943602183
2024-09-27 16:54:44,016 | INFO | With p = 0.6 = 3 / 5, training 46 instead
Idx = 46, accuracy = 0.10491206791995149
2024-09-27 16:54:48,853 | INFO | With p = 1.0 = 5 / 5, mutating 50 to 56
Idx = 56, accuracy = 0.41449363250454824
2024-09-27 16:54:54,260 | INFO | With p = 1.0 = 5 / 5, mutating 50 to 57
Idx = 57, accuracy = 0.15251667677380232
2024-09-27 16:54:59,452 | INFO | With p = 1.0 = 5 / 5, mutating 51 to 58
Idx = 58, accuracy = 0.5209217707701638
2024-09-27 16:55:06,392 | INFO | With p = 1.0 = 5 / 5, mutating 50 to 59
Idx = 59, accuracy = 0.8580958156458459
2024-09-27 16:55:13,121 | INFO | With p = 0.2 = 1 / 5, training 59 instead
Idx = 59, accuracy = 0.8744693753790176
2024-09-27 16:55:20,769 | INFO | With p = 0.4 = 2 / 5, training 59 instead
Idx = 59, accuracy = 0.8899332929047907
2024-09-27 16:55:27,426 | INFO | With p = 0.6 = 3 / 5, mutating 59 to 60
Idx = 60, accuracy = 0.8914493632504549
2024-09-27 16:55:33,794 | INFO | With p = 0.2 = 1 / 5, mutating 60 to 61
Idx = 61, accuracy = 0.09187386294724076
2024-09-27 16:55:42,108 | INFO | With p = 0.2 = 1 / 5, mutating 60 to 62
Idx = 62, accuracy = 0.10430563978168587
2024-09-27 16:55:48,857 | INFO | With p = 0.2 = 1 / 5, training 60 instead
Idx = 60, accuracy = 0.10430563978168587
2024-09-27 16:55:54,703 | INFO | With p = 0.6 = 3 / 5, training 59 instead
Idx = 59, accuracy = 0.10430563978168587
2024-09-27 16:56:00,531 | INFO | With p = 1.0 = 5 / 5, mutating 51 to 63
Idx = 63, accuracy = 0.10430563978168587
2024-09-27 16:56:07,089 | INFO | With p = 1.0 = 5 / 5, mutating 50 to 64
Idx = 64, accuracy = 0.10430563978168587
2024-09-27 16:56:13,573 | INFO | With p = 1.0 = 5 / 5, mutating 51 to 65
Idx = 65, accuracy = 0.10430563978168587
2024-09-27 16:56:20,029 | INFO | With p = 1.0 = 5 / 5, mutating 50 to 66
Idx = 66, accuracy = 0.10430563978168587
2024-09-27 16:56:26,692 | INFO | With p = 1.0 = 5 / 5, mutating 51 to 67
Idx = 67, accuracy = 0.10430563978168587
2024-09-27 16:56:33,316 | INFO | With p = 1.0 = 5 / 5, mutating 50 to 68
Idx = 68, accuracy = 0.10430563978168587
2024-09-27 16:56:40,921 | INFO | With p = 1.0 = 5 / 5, mutating 51 to 69
Idx = 69, accuracy = 0.10430563978168587
2024-09-27 16:56:48,450 | INFO | With p = 1.0 = 5 / 5, mutating 50 to 70
Idx = 70, accuracy = 0.10430563978168587
2024-09-27 16:56:55,161 | INFO | With p = 1.0 = 5 / 5, mutating 51 to 71
Idx = 71, accuracy = 0.10491206791995149
2024-09-27 16:56:59,954 | INFO | With p = 1.0 = 5 / 5, mutating 50 to 72
Idx = 72, accuracy = 0.10491206791995149
2024-09-27 16:57:04,735 | INFO | With p = 1.0 = 5 / 5, mutating 51 to 73
Idx = 73, accuracy = 0.10491206791995149
2024-09-27 16:57:08,903 | INFO | With p = 1.0 = 5 / 5, mutating 49 to 74
Idx = 74, accuracy = 0.10491206791995149
2024-09-27 16:57:11,916 | INFO | With p = 1.0 = 5 / 5, mutating 50 to 75
Idx = 75, accuracy = 0.10491206791995149
2024-09-27 16:57:16,164 | INFO | With p = 1.0 = 5 / 5, mutating 51 to 76
Idx = 76, accuracy = 0.12795633717404487
2024-09-27 16:57:20,839 | INFO | With p = 1.0 = 5 / 5, mutating 50 to 77
Idx = 77, accuracy = 0.18374772589448152
2024-09-27 16:57:25,489 | INFO | With p = 1.0 = 5 / 5, mutating 51 to 78
Idx = 78, accuracy = 0.18374772589448152
2024-09-27 16:57:30,103 | INFO | With p = 1.0 = 5 / 5, mutating 49 to 79
Idx = 79, accuracy = 0.18374772589448152
2024-09-27 16:57:34,800 | INFO | With p = 1.0 = 5 / 5, mutating 50 to 80
Idx = 80, accuracy = 0.12219526986052152
2024-09-27 16:57:39,801 | INFO | With p = 1.0 = 5 / 5, mutating 51 to 81
Idx = 81, accuracy = 0.13644633110976348
2024-09-27 16:57:44,826 | INFO | With p = 1.0 = 5 / 5, mutating 50 to 82
Idx = 82, accuracy = 0.10006064281382657
2024-09-27 16:57:49,986 | INFO | With p = 1.0 = 5 / 5, mutating 51 to 83
Idx = 83, accuracy = 0.08641600970285021
2024-09-27 16:57:56,756 | INFO | With p = 1.0 = 5 / 5, mutating 49 to 84
Idx = 84, accuracy = 0.10036385688295937
2024-09-27 16:58:02,247 | INFO | With p = 1.0 = 5 / 5, mutating 50 to 85
Idx = 85, accuracy = 0.09915100060642813
2024-09-27 16:58:07,702 | INFO | With p = 1.0 = 5 / 5, mutating 51 to 86
Idx = 86, accuracy = 0.07489387507580351
2024-09-27 16:58:12,574 | INFO | With p = 1.0 = 5 / 5, mutating 50 to 87
Idx = 87, accuracy = 0.11006670709520922
2024-09-27 16:58:17,350 | INFO | With p = 1.0 = 5 / 5, mutating 51 to 88
Idx = 88, accuracy = 0.1052152819890843
2024-09-27 16:58:22,176 | INFO | With p = 1.0 = 5 / 5, mutating 50 to 89
Idx = 89, accuracy = 0.10066707095209218
2024-09-27 16:58:26,661 | INFO | With p = 1.0 = 5 / 5, mutating 49 to 90
Idx = 90, accuracy = 0.10066707095209218
2024-09-27 16:58:31,069 | INFO | With p = 1.0 = 5 / 5, mutating 51 to 91
Idx = 91, accuracy = 0.10066707095209218
2024-09-27 16:58:35,554 | INFO | With p = 1.0 = 5 / 5, mutating 50 to 92
Idx = 92, accuracy = 0.10430563978168587
2024-09-27 16:58:40,277 | INFO | With p = 1.0 = 5 / 5, mutating 51 to 93
Idx = 93, accuracy = 0.09945421467556094
2024-09-27 16:58:46,086 | INFO | With p = 1.0 = 5 / 5, mutating 50 to 94
Idx = 94, accuracy = 0.09945421467556094
2024-09-27 16:58:51,984 | INFO | With p = 1.0 = 5 / 5, mutating 51 to 95
Idx = 95, accuracy = 0.09945421467556094
2024-09-27 16:58:57,496 | INFO | With p = 1.0 = 5 / 5, mutating 49 to 96
Idx = 96, accuracy = 0.0979381443298969
2024-09-27 16:59:02,098 | INFO | With p = 1.0 = 5 / 5, mutating 50 to 97
Idx = 97, accuracy = 0.15221346270466948
2024-09-27 16:59:07,683 | INFO | With p = 1.0 = 5 / 5, mutating 51 to 98
Idx = 98, accuracy = 0.10430563978168587
2024-09-27 16:59:12,929 | INFO | With p = 1.0 = 5 / 5, mutating 50 to 99
Idx = 99, accuracy = 0.10430563978168587
2024-09-27 16:59:18,076 | INFO | With p = 1.0 = 5 / 5, mutating 51 to 100
Idx = 100, accuracy = 0.10430563978168587
2024-09-27 16:59:23,240 | INFO | With p = 1.0 = 5 / 5, mutating 50 to 101
Idx = 101, accuracy = 0.10430563978168587
2024-09-27 16:59:27,991 | INFO | With p = 1.0 = 5 / 5, mutating 51 to 102
Idx = 102, accuracy = 0.1052152819890843
2024-09-27 16:59:32,129 | INFO | With p = 1.0 = 5 / 5, mutating 49 to 103
Idx = 103, accuracy = 0.1052152819890843
2024-09-27 16:59:36,299 | INFO | With p = 1.0 = 5 / 5, mutating 50 to 104
Idx = 104, accuracy = 0.09945421467556094
2024-09-27 16:59:40,025 | INFO | With p = 1.0 = 5 / 5, mutating 51 to 105
Idx = 105, accuracy = 0.08793208004851426
2024-09-27 16:59:43,193 | INFO | With p = 1.0 = 5 / 5, mutating 50 to 106
Idx = 106, accuracy = 0.08793208004851426
2024-09-27 16:59:46,363 | INFO | With p = 1.0 = 5 / 5, mutating 51 to 107
Idx = 107, accuracy = 0.09945421467556094
2024-09-27 16:59:47,858 | INFO | With p = 1.0 = 5 / 5, mutating 49 to 108
Idx = 108, accuracy = 0.09945421467556094
2024-09-27 16:59:49,319 | INFO | With p = 1.0 = 5 / 5, mutating 50 to 109
Idx = 109, accuracy = 0.11885991510006064
2024-09-27 16:59:50,678 | INFO | With p = 1.0 = 5 / 5, mutating 51 to 110
Idx = 110, accuracy = 0.12249848392965433
2024-09-27 16:59:52,056 | INFO | With p = 1.0 = 5 / 5, mutating 50 to 111
Idx = 111, accuracy = 0.11976955730745907
2024-09-27 16:59:54,070 | INFO | With p = 1.0 = 5 / 5, mutating 51 to 112
Idx = 112, accuracy = 0.13189812007277138
2024-09-27 16:59:56,541 | INFO | With p = 1.0 = 5 / 5, mutating 50 to 113
Idx = 113, accuracy = 0.13189812007277138
2024-09-27 16:59:59,002 | INFO | With p = 1.0 = 5 / 5, mutating 51 to 114
Idx = 114, accuracy = 0.12856276531231048
2024-09-27 17:00:01,637 | INFO | With p = 1.0 = 5 / 5, mutating 49 to 115
Idx = 115, accuracy = 0.16919345057610674
2024-09-27 17:00:04,269 | INFO | With p = 1.0 = 5 / 5, mutating 50 to 116
Idx = 116, accuracy = 0.1722255912674348
2024-09-27 17:00:07,117 | INFO | With p = 1.0 = 5 / 5, mutating 51 to 117
Idx = 117, accuracy = 0.1331109763493026
2024-09-27 17:00:09,766 | INFO | With p = 1.0 = 5 / 5, mutating 50 to 118
Idx = 118, accuracy = 0.13978168587022438
2024-09-27 17:00:12,455 | INFO | With p = 1.0 = 5 / 5, mutating 51 to 119
Idx = 119, accuracy = 0.13978168587022438
2024-09-27 17:00:14,902 | INFO | With p = 1.0 = 5 / 5, mutating 50 to 120
Idx = 120, accuracy = 0.1409945421467556
2024-09-27 17:00:17,433 | INFO | With p = 1.0 = 5 / 5, mutating 51 to 121
Idx = 121, accuracy = 0.14614918132201335
2024-09-27 17:00:19,619 | INFO | With p = 1.0 = 5 / 5, mutating 49 to 122
Idx = 122, accuracy = 0.1409945421467556
2024-09-27 17:00:21,479 | INFO | With p = 1.0 = 5 / 5, mutating 50 to 123
Idx = 123, accuracy = 0.1409945421467556
2024-09-27 17:00:23,326 | INFO | With p = 1.0 = 5 / 5, mutating 51 to 124
Idx = 124, accuracy = 0.07610673135233474
2024-09-27 17:00:26,055 | INFO | With p = 1.0 = 5 / 5, mutating 50 to 125
Idx = 125, accuracy = 0.0979381443298969
2024-09-27 17:00:28,501 | INFO | With p = 1.0 = 5 / 5, mutating 51 to 126
Idx = 126, accuracy = 0.0979381443298969
2024-09-27 17:00:30,961 | INFO | With p = 1.0 = 5 / 5, mutating 50 to 127
Idx = 127, accuracy = 0.03365676167374166
2024-09-27 17:00:34,404 | INFO | With p = 1.0 = 5 / 5, mutating 51 to 128
Idx = 128, accuracy = 0.03365676167374166
2024-09-27 17:00:37,859 | INFO | With p = 1.0 = 5 / 5, mutating 50 to 129
Idx = 129, accuracy = 0.03365676167374166
2024-09-27 17:00:41,287 | INFO | With p = 1.0 = 5 / 5, mutating 49 to 130
Idx = 130, accuracy = 0.03365676167374166
2024-09-27 17:00:44,683 | INFO | With p = 1.0 = 5 / 5, mutating 51 to 131
Idx = 131, accuracy = 0.03365676167374166
2024-09-27 17:00:48,142 | INFO | With p = 1.0 = 5 / 5, mutating 50 to 132
Idx = 132, accuracy = 0.10066707095209218
2024-09-27 17:00:52,585 | INFO | With p = 1.0 = 5 / 5, mutating 51 to 133
Idx = 133, accuracy = 0.10097028502122499
2024-09-27 17:00:57,922 | INFO | With p = 1.0 = 5 / 5, mutating 50 to 134
Idx = 134, accuracy = 0.10097028502122499
2024-09-27 17:01:03,424 | INFO | With p = 1.0 = 5 / 5, mutating 51 to 135
Idx = 135, accuracy = 0.10097028502122499
2024-09-27 17:01:08,907 | INFO | With p = 1.0 = 5 / 5, mutating 50 to 136
Idx = 136, accuracy = 0.10491206791995149
2024-09-27 17:01:14,405 | INFO | With p = 1.0 = 5 / 5, mutating 51 to 137
Idx = 137, accuracy = 0.10491206791995149
2024-09-27 17:01:19,904 | INFO | With p = 1.0 = 5 / 5, mutating 49 to 138
Idx = 138, accuracy = 0.10097028502122499
2024-09-27 17:01:25,810 | INFO | With p = 1.0 = 5 / 5, mutating 50 to 139
Idx = 139, accuracy = 0.11249241964827168
2024-09-27 17:01:32,185 | INFO | With p = 1.0 = 5 / 5, mutating 51 to 140
Idx = 140, accuracy = 0.11249241964827168
2024-09-27 17:01:38,559 | INFO | With p = 1.0 = 5 / 5, mutating 50 to 141
Idx = 141, accuracy = 0.11249241964827168
2024-09-27 17:01:44,809 | INFO | With p = 1.0 = 5 / 5, mutating 51 to 142
Idx = 142, accuracy = 0.11249241964827168
2024-09-27 17:01:51,080 | INFO | With p = 1.0 = 5 / 5, mutating 50 to 143
Idx = 143, accuracy = 0.11249241964827168
2024-09-27 17:01:57,292 | INFO | With p = 1.0 = 5 / 5, mutating 51 to 144
Idx = 144, accuracy = 0.11249241964827168
2024-09-27 17:02:03,631 | INFO | With p = 1.0 = 5 / 5, mutating 49 to 145
Idx = 145, accuracy = 0.10491206791995149
2024-09-27 17:02:09,974 | INFO | With p = 1.0 = 5 / 5, mutating 50 to 146
Idx = 146, accuracy = 0.10430563978168587
2024-09-27 17:02:14,639 | INFO | With p = 1.0 = 5 / 5, mutating 51 to 147
Idx = 147, accuracy = 0.17465130382049726
2024-09-27 17:02:20,797 | INFO | With p = 1.0 = 5 / 5, mutating 50 to 148
Idx = 148, accuracy = 0.03820497271073378
2024-09-27 17:02:26,547 | INFO | With p = 1.0 = 5 / 5, mutating 51 to 149
Idx = 149, accuracy = 0.03820497271073378
2024-09-27 17:02:32,244 | INFO | With p = 1.0 = 5 / 5, mutating 50 to 150
Idx = 150, accuracy = 0.03820497271073378
2024-09-27 17:02:37,955 | INFO | With p = 1.0 = 5 / 5, mutating 51 to 151
Idx = 151, accuracy = 0.07186173438447543
2024-09-27 17:02:43,338 | INFO | With p = 1.0 = 5 / 5, mutating 50 to 152
Idx = 152, accuracy = 0.10491206791995149
2024-09-27 17:02:48,572 | INFO | With p = 1.0 = 5 / 5, mutating 49 to 153
Idx = 153, accuracy = 0.10491206791995149
2024-09-27 17:02:53,750 | INFO | With p = 1.0 = 5 / 5, mutating 51 to 154
Idx = 154, accuracy = 0.09945421467556094
2024-09-27 17:02:59,071 | INFO | With p = 1.0 = 5 / 5, mutating 50 to 155
Idx = 155, accuracy = 0.09157064887810794
2024-09-27 17:03:05,324 | INFO | With p = 1.0 = 5 / 5, mutating 51 to 156
Idx = 156, accuracy = 0.16070345664038813
2024-09-27 17:03:11,499 | INFO | With p = 1.0 = 5 / 5, mutating 50 to 157
Idx = 157, accuracy = 0.16070345664038813
2024-09-27 17:03:17,681 | INFO | With p = 1.0 = 5 / 5, mutating 51 to 158
Idx = 158, accuracy = 0.31443298969072164
2024-09-27 17:03:23,086 | INFO | With p = 1.0 = 5 / 5, mutating 50 to 159
Idx = 159, accuracy = 0.09945421467556094
2024-09-27 17:03:26,615 | INFO | With p = 1.0 = 5 / 5, mutating 51 to 160
Idx = 160, accuracy = 0.09945421467556094
2024-09-27 17:03:30,107 | INFO | With p = 1.0 = 5 / 5, mutating 50 to 161
Idx = 161, accuracy = 0.09945421467556094
2024-09-27 17:03:33,657 | INFO | With p = 1.0 = 5 / 5, mutating 49 to 162
Idx = 162, accuracy = 0.09945421467556094
2024-09-27 17:03:37,158 | INFO | With p = 1.0 = 5 / 5, mutating 51 to 163
Idx = 163, accuracy = 0.11946634323832626
2024-09-27 17:03:42,396 | INFO | With p = 1.0 = 5 / 5, mutating 50 to 164
Idx = 164, accuracy = 0.031534263189812006
2024-09-27 17:03:47,390 | INFO | With p = 1.0 = 5 / 5, mutating 51 to 165
Idx = 165, accuracy = 0.031534263189812006
2024-09-27 17:03:52,381 | INFO | With p = 1.0 = 5 / 5, mutating 50 to 166
Idx = 166, accuracy = 0.031534263189812006
2024-09-27 17:03:57,397 | INFO | With p = 1.0 = 5 / 5, mutating 51 to 167
Idx = 167, accuracy = 0.031534263189812006
2024-09-27 17:04:02,560 | INFO | With p = 1.0 = 5 / 5, mutating 50 to 168
Idx = 168, accuracy = 0.156155245603396
2024-09-27 17:04:06,986 | INFO | With p = 1.0 = 5 / 5, mutating 51 to 169
Idx = 169, accuracy = 0.10491206791995149
2024-09-27 17:04:11,899 | INFO | With p = 1.0 = 5 / 5, mutating 49 to 170
Idx = 170, accuracy = 0.6819284414796847
2024-09-27 17:04:16,511 | INFO | With p = 1.0 = 5 / 5, mutating 50 to 171
Idx = 171, accuracy = 0.8489993935718617
2024-09-27 17:04:21,109 | INFO | With p = 0.2 = 1 / 5, training 171 instead
Idx = 171, accuracy = 0.8699211643420255
2024-09-27 17:04:25,787 | INFO | With p = 0.4 = 2 / 5, training 171 instead
Idx = 171, accuracy = 0.8787143723468769
2024-09-27 17:04:30,503 | INFO | With p = 0.6 = 3 / 5, mutating 171 to 172
Idx = 172, accuracy = 0.8808368708308065
2024-09-27 17:04:35,130 | INFO | With p = 0.2 = 1 / 5, training 172 instead
Idx = 172, accuracy = 0.8829593693147362
2024-09-27 17:04:39,790 | INFO | With p = 0.4 = 2 / 5, training 172 instead
Idx = 172, accuracy = 0.883262583383869
2024-09-27 17:04:44,455 | INFO | With p = 0.6 = 3 / 5, mutating 172 to 173
Idx = 173, accuracy = 0.8850818677986658
2024-09-27 17:04:49,131 | INFO | With p = 0.2 = 1 / 5, training 173 instead
Idx = 173, accuracy = 0.8771983020012128
2024-09-27 17:04:53,832 | INFO | With p = 0.4 = 2 / 5, training 173 instead
Idx = 173, accuracy = 0.8811400848999393
2024-09-27 17:04:58,527 | INFO | With p = 0.6 = 3 / 5, training 173 instead
Idx = 173, accuracy = 0.8844754396604002
2024-09-27 17:05:03,197 | INFO | With p = 0.6 = 3 / 5, mutating 172 to 174
Idx = 174, accuracy = 0.09702850212249849
2024-09-27 17:05:08,061 | INFO | With p = 0.8 = 4 / 5, mutating 173 to 175
Idx = 175, accuracy = 0.10855063674954518
2024-09-27 17:05:12,848 | INFO | With p = 0.6 = 3 / 5, mutating 172 to 176
Idx = 176, accuracy = 0.8035172832019406
2024-09-27 17:05:16,063 | INFO | With p = 0.8 = 4 / 5, mutating 173 to 177
Idx = 177, accuracy = 0.8156458459672529
2024-09-27 17:05:19,161 | INFO | With p = 0.6 = 3 / 5, mutating 172 to 178
Idx = 178, accuracy = 0.823226197695573
2024-09-27 17:05:22,247 | INFO | With p = 0.2 = 1 / 5, training 178 instead
Idx = 178, accuracy = 0.8302001212856277
2024-09-27 17:05:25,366 | INFO | With p = 0.8 = 4 / 5, training 173 instead
Idx = 173, accuracy = 0.8335354760460886
2024-09-27 17:05:28,467 | INFO | With p = 0.6 = 3 / 5, mutating 172 to 179
Idx = 179, accuracy = 0.10006064281382657
2024-09-27 17:05:32,413 | INFO | With p = 0.6 = 3 / 5, training 172 instead
Idx = 172, accuracy = 0.10006064281382657
2024-09-27 17:05:36,448 | INFO | With p = 0.6 = 3 / 5, mutating 171 to 180
Idx = 180, accuracy = 0.10006064281382657
2024-09-27 17:05:40,478 | INFO | With p = 0.2 = 1 / 5, training 177 instead
Idx = 177, accuracy = 0.10006064281382657
2024-09-27 17:05:44,532 | INFO | With p = 0.6 = 3 / 5, training 171 instead
Idx = 171, accuracy = 0.10006064281382657
2024-09-27 17:05:48,595 | INFO | With p = 1.0 = 5 / 5, mutating 173 to 181
Idx = 181, accuracy = 0.10006064281382657
2024-09-27 17:05:52,686 | INFO | With p = 1.0 = 5 / 5, mutating 173 to 182
Idx = 182, accuracy = 0.09915100060642813
2024-09-27 17:05:56,569 | INFO | With p = 1.0 = 5 / 5, mutating 173 to 183
Idx = 183, accuracy = 0.09945421467556094
2024-09-27 17:06:00,420 | INFO | With p = 1.0 = 5 / 5, mutating 173 to 184
Idx = 184, accuracy = 0.09945421467556094
2024-09-27 17:06:03,396 | INFO | With p = 0.2 = 1 / 5, training 176 instead
Idx = 176, accuracy = 0.09945421467556094
2024-09-27 17:06:06,371 | INFO | With p = 1.0 = 5 / 5, mutating 173 to 185
Idx = 185, accuracy = 0.09945421467556094
2024-09-27 17:06:08,641 | INFO | With p = 1.0 = 5 / 5, mutating 173 to 186
Idx = 186, accuracy = 0.09945421467556094
2024-09-27 17:06:10,871 | INFO | With p = 1.0 = 5 / 5, mutating 173 to 187
Idx = 187, accuracy = 0.09945421467556094
2024-09-27 17:06:12,653 | INFO | With p = 1.0 = 5 / 5, mutating 173 to 188
Idx = 188, accuracy = 0.09945421467556094
2024-09-27 17:06:14,402 | INFO | With p = 1.0 = 5 / 5, mutating 173 to 189
Idx = 189, accuracy = 0.09945421467556094
2024-09-27 17:06:18,547 | INFO | With p = 0.4 = 2 / 5, training 178 instead
Idx = 178, accuracy = 0.09945421467556094
2024-09-27 17:06:22,746 | INFO | With p = 1.0 = 5 / 5, mutating 173 to 190
Idx = 190, accuracy = 0.09945421467556094
2024-09-27 17:06:28,100 | INFO | With p = 1.0 = 5 / 5, mutating 173 to 191
Idx = 191, accuracy = 0.09945421467556094
2024-09-27 17:06:33,385 | INFO | With p = 1.0 = 5 / 5, mutating 173 to 192
Idx = 192, accuracy = 0.09945421467556094
2024-09-27 17:06:38,637 | INFO | With p = 1.0 = 5 / 5, mutating 173 to 193
Idx = 193, accuracy = 0.09945421467556094
2024-09-27 17:06:43,644 | INFO | With p = 1.0 = 5 / 5, mutating 173 to 194
Idx = 194, accuracy = 0.09945421467556094
2024-09-27 17:06:49,097 | INFO | With p = 1.0 = 5 / 5, mutating 173 to 195
Idx = 195, accuracy = 0.046694966646452396
2024-09-27 17:06:54,419 | INFO | With p = 1.0 = 5 / 5, mutating 173 to 196
Idx = 196, accuracy = 0.046694966646452396
2024-09-27 17:06:59,700 | INFO | With p = 1.0 = 5 / 5, mutating 173 to 197
Idx = 197, accuracy = 0.046694966646452396
2024-09-27 17:07:04,966 | INFO | With p = 1.0 = 5 / 5, mutating 173 to 198
Idx = 198, accuracy = 0.15706488781079442
2024-09-27 17:07:08,975 | INFO | With p = 1.0 = 5 / 5, mutating 173 to 199
Idx = 199, accuracy = 0.15706488781079442
2024-09-27 17:07:12,962 | INFO | With p = 1.0 = 5 / 5, mutating 173 to 200
Idx = 200, accuracy = 0.15706488781079442
2024-09-27 17:07:16,917 | INFO | With p = 1.0 = 5 / 5, mutating 173 to 201
Idx = 201, accuracy = 0.09824135839902971
2024-09-27 17:07:20,023 | INFO | With p = 1.0 = 5 / 5, mutating 173 to 202
Idx = 202, accuracy = 0.09824135839902971
2024-09-27 17:07:23,094 | INFO | With p = 1.0 = 5 / 5, mutating 173 to 203
Idx = 203, accuracy = 0.10278956943602183
2024-09-27 17:07:26,120 | INFO | With p = 1.0 = 5 / 5, mutating 173 to 204
Idx = 204, accuracy = 0.10278956943602183
2024-09-27 17:07:29,349 | INFO | With p = 1.0 = 5 / 5, mutating 173 to 205
Idx = 205, accuracy = 0.10278956943602183
2024-09-27 17:07:32,562 | INFO | With p = 1.0 = 5 / 5, mutating 173 to 206
Idx = 206, accuracy = 0.10278956943602183
2024-09-27 17:07:35,770 | INFO | With p = 1.0 = 5 / 5, mutating 173 to 207
Idx = 207, accuracy = 0.10278956943602183
2024-09-27 17:07:39,010 | INFO | With p = 1.0 = 5 / 5, mutating 173 to 208
Idx = 208, accuracy = 0.10278956943602183
2024-09-27 17:07:42,149 | INFO | With p = 1.0 = 5 / 5, mutating 173 to 209
Idx = 209, accuracy = 0.10278956943602183
2024-09-27 17:07:45,300 | INFO | With p = 1.0 = 5 / 5, mutating 173 to 210
Idx = 210, accuracy = 0.09945421467556094
2024-09-27 17:07:47,253 | INFO | With p = 1.0 = 5 / 5, mutating 173 to 211
Idx = 211, accuracy = 0.10278956943602183
2024-09-27 17:07:48,508 | INFO | With p = 1.0 = 5 / 5, mutating 173 to 212
Idx = 212, accuracy = 0.09945421467556094
2024-09-27 17:07:51,280 | INFO | With p = 1.0 = 5 / 5, mutating 173 to 213
Idx = 213, accuracy = 0.09945421467556094
2024-09-27 17:07:54,457 | INFO | With p = 1.0 = 5 / 5, mutating 173 to 214
Idx = 214, accuracy = 0.09945421467556094
2024-09-27 17:07:57,583 | INFO | With p = 1.0 = 5 / 5, mutating 173 to 215
Idx = 215, accuracy = 0.09945421467556094
2024-09-27 17:08:01,621 | INFO | With p = 1.0 = 5 / 5, mutating 173 to 216
Idx = 216, accuracy = 0.09945421467556094
2024-09-27 17:08:04,447 | INFO | With p = 1.0 = 5 / 5, mutating 173 to 217
Idx = 217, accuracy = 0.09945421467556094
2024-09-27 17:08:07,314 | INFO | With p = 1.0 = 5 / 5, mutating 173 to 218
Idx = 218, accuracy = 0.09945421467556094
2024-09-27 17:08:10,312 | INFO | With p = 1.0 = 5 / 5, mutating 173 to 219
Idx = 219, accuracy = 0.09945421467556094
2024-09-27 17:08:13,335 | INFO | With p = 1.0 = 5 / 5, mutating 173 to 220
Idx = 220, accuracy = 0.09945421467556094
2024-09-27 17:08:16,265 | INFO | With p = 1.0 = 5 / 5, mutating 173 to 221
Idx = 221, accuracy = 0.09945421467556094
2024-09-27 17:08:18,517 | INFO | With p = 1.0 = 5 / 5, mutating 173 to 222
Idx = 222, accuracy = 0.09945421467556094
2024-09-27 17:08:21,787 | INFO | With p = 1.0 = 5 / 5, mutating 173 to 223
Idx = 223, accuracy = 0.09945421467556094
2024-09-27 17:08:24,628 | INFO | With p = 1.0 = 5 / 5, mutating 173 to 224
Idx = 224, accuracy = 0.09945421467556094
2024-09-27 17:08:27,277 | INFO | With p = 1.0 = 5 / 5, mutating 173 to 225
Idx = 225, accuracy = 0.07580351728320193
2024-09-27 17:08:29,254 | INFO | With p = 1.0 = 5 / 5, mutating 173 to 226
Idx = 226, accuracy = 0.0861127956337174
2024-09-27 17:08:31,183 | INFO | With p = 1.0 = 5 / 5, mutating 173 to 227
Idx = 227, accuracy = 0.10278956943602183
2024-09-27 17:08:33,197 | INFO | With p = 1.0 = 5 / 5, mutating 173 to 228
Idx = 228, accuracy = 0.09945421467556094
2024-09-27 17:08:36,443 | INFO | With p = 1.0 = 5 / 5, mutating 173 to 229
Idx = 229, accuracy = 0.09945421467556094
2024-09-27 17:08:39,681 | INFO | With p = 1.0 = 5 / 5, mutating 173 to 230
Idx = 230, accuracy = 0.09945421467556094
2024-09-27 17:08:42,972 | INFO | With p = 1.0 = 5 / 5, mutating 173 to 231
Idx = 231, accuracy = 0.09945421467556094
2024-09-27 17:08:46,265 | INFO | With p = 1.0 = 5 / 5, mutating 173 to 232
Idx = 232, accuracy = 0.09945421467556094
2024-09-27 17:08:49,606 | INFO | With p = 1.0 = 5 / 5, mutating 173 to 233
Idx = 233, accuracy = 0.09945421467556094
2024-09-27 17:08:52,991 | INFO | With p = 1.0 = 5 / 5, mutating 173 to 234
Idx = 234, accuracy = 0.09945421467556094
2024-09-27 17:08:56,328 | INFO | With p = 1.0 = 5 / 5, mutating 173 to 235
Idx = 235, accuracy = 0.09945421467556094
2024-09-27 17:08:59,659 | INFO | With p = 1.0 = 5 / 5, mutating 173 to 236
Idx = 236, accuracy = 0.09945421467556094
2024-09-27 17:09:02,961 | INFO | With p = 1.0 = 5 / 5, mutating 173 to 237
Idx = 237, accuracy = 0.09945421467556094
2024-09-27 17:09:09,184 | INFO | With p = 1.0 = 5 / 5, mutating 173 to 238
Idx = 238, accuracy = 0.09945421467556094
2024-09-27 17:09:15,328 | INFO | With p = 1.0 = 5 / 5, mutating 173 to 239
Idx = 239, accuracy = 0.09945421467556094
2024-09-27 17:09:21,496 | INFO | With p = 1.0 = 5 / 5, mutating 173 to 240
Idx = 240, accuracy = 0.09945421467556094
2024-09-27 17:09:27,667 | INFO | With p = 1.0 = 5 / 5, mutating 173 to 241
Idx = 241, accuracy = 0.09945421467556094
2024-09-27 17:09:33,822 | INFO | With p = 1.0 = 5 / 5, mutating 173 to 242
Idx = 242, accuracy = 0.09945421467556094
2024-09-27 17:09:38,383 | INFO | With p = 1.0 = 5 / 5, mutating 173 to 243
Idx = 243, accuracy = 0.09945421467556094
2024-09-27 17:09:42,918 | INFO | With p = 1.0 = 5 / 5, mutating 173 to 244
Idx = 244, accuracy = 0.09945421467556094
2024-09-27 17:09:46,692 | INFO | With p = 1.0 = 5 / 5, mutating 173 to 245
Idx = 245, accuracy = 0.09945421467556094
2024-09-27 17:09:50,444 | INFO | With p = 1.0 = 5 / 5, mutating 173 to 246
Idx = 246, accuracy = 0.09945421467556094
2024-09-27 17:09:54,712 | INFO | With p = 1.0 = 5 / 5, mutating 173 to 247
Idx = 247, accuracy = 0.09945421467556094
2024-09-27 17:09:58,980 | INFO | With p = 1.0 = 5 / 5, mutating 173 to 248
Idx = 248, accuracy = 0.09945421467556094
2024-09-27 17:10:03,199 | INFO | With p = 1.0 = 5 / 5, mutating 173 to 249
Idx = 249, accuracy = 0.09945421467556094
2024-09-27 17:10:07,459 | INFO | With p = 1.0 = 5 / 5, mutating 173 to 250
Idx = 250, accuracy = 0.09945421467556094
2024-09-27 17:10:11,785 | INFO | With p = 1.0 = 5 / 5, mutating 173 to 251
Idx = 251, accuracy = 0.6161309884778654
2024-09-27 17:10:16,145 | INFO | With p = 1.0 = 5 / 5, mutating 173 to 252
Idx = 252, accuracy = 0.1052152819890843
2024-09-27 17:10:20,539 | INFO | With p = 1.0 = 5 / 5, mutating 173 to 253
Idx = 253, accuracy = 0.1052152819890843
2024-09-27 17:10:24,986 | INFO | With p = 1.0 = 5 / 5, mutating 173 to 254
Idx = 254, accuracy = 0.1052152819890843
2024-09-27 17:10:29,349 | INFO | With p = 1.0 = 5 / 5, mutating 173 to 255
Idx = 255, accuracy = 0.1052152819890843
2024-09-27 17:10:33,757 | INFO | With p = 1.0 = 5 / 5, mutating 173 to 256
Idx = 256, accuracy = 0.1052152819890843
2024-09-27 17:10:38,218 | INFO | With p = 1.0 = 5 / 5, mutating 173 to 257
Idx = 257, accuracy = 0.09884778653729533
2024-09-27 17:10:42,739 | INFO | With p = 1.0 = 5 / 5, mutating 173 to 258
Idx = 258, accuracy = 0.09945421467556094
2024-09-27 17:10:46,550 | INFO | With p = 1.0 = 5 / 5, mutating 173 to 259
Idx = 259, accuracy = 0.15160703456640387
2024-09-27 17:10:51,478 | INFO | With p = 1.0 = 5 / 5, mutating 173 to 260
Idx = 260, accuracy = 0.15160703456640387
2024-09-27 17:10:56,380 | INFO | With p = 1.0 = 5 / 5, mutating 173 to 261
Idx = 261, accuracy = 0.1052152819890843
2024-09-27 17:11:01,396 | INFO | With p = 1.0 = 5 / 5, mutating 173 to 262
Idx = 262, accuracy = 0.1052152819890843
2024-09-27 17:11:06,469 | INFO | With p = 1.0 = 5 / 5, mutating 173 to 263
Idx = 263, accuracy = 0.1052152819890843
2024-09-27 17:11:11,495 | INFO | With p = 1.0 = 5 / 5, mutating 173 to 264
Idx = 264, accuracy = 0.1052152819890843
2024-09-27 17:11:16,323 | INFO | With p = 1.0 = 5 / 5, mutating 173 to 265
Idx = 265, accuracy = 0.10278956943602183
2024-09-27 17:11:21,336 | INFO | With p = 1.0 = 5 / 5, mutating 173 to 266
Idx = 266, accuracy = 0.10278956943602183
2024-09-27 17:11:26,333 | INFO | With p = 1.0 = 5 / 5, mutating 173 to 267
Idx = 267, accuracy = 0.10278956943602183
2024-09-27 17:11:31,328 | INFO | With p = 1.0 = 5 / 5, mutating 173 to 268
Idx = 268, accuracy = 0.10278956943602183
2024-09-27 17:11:36,335 | INFO | With p = 1.0 = 5 / 5, mutating 173 to 269
Idx = 269, accuracy = 0.10278956943602183
2024-09-27 17:11:41,315 | INFO | With p = 1.0 = 5 / 5, mutating 173 to 270
Idx = 270, accuracy = 0.09945421467556094
2024-09-27 17:11:46,517 | INFO | With p = 1.0 = 5 / 5, mutating 173 to 271
Idx = 271, accuracy = 0.09945421467556094
2024-09-27 17:11:51,753 | INFO | With p = 1.0 = 5 / 5, mutating 173 to 272
Idx = 272, accuracy = 0.09945421467556094
2024-09-27 17:11:57,868 | INFO | With p = 1.0 = 5 / 5, mutating 173 to 273
Idx = 273, accuracy = 0.09945421467556094
2024-09-27 17:12:03,947 | INFO | With p = 1.0 = 5 / 5, mutating 173 to 274
Idx = 274, accuracy = 0.09945421467556094
2024-09-27 17:12:10,881 | INFO | With p = 1.0 = 5 / 5, mutating 173 to 275
Idx = 275, accuracy = 0.09945421467556094
2024-09-27 17:12:15,807 | INFO | With p = 1.0 = 5 / 5, mutating 173 to 276
Idx = 276, accuracy = 0.09945421467556094
2024-09-27 17:12:20,748 | INFO | With p = 1.0 = 5 / 5, mutating 173 to 277
Idx = 277, accuracy = 0.09945421467556094
2024-09-27 17:12:27,157 | INFO | With p = 1.0 = 5 / 5, mutating 173 to 278
Idx = 278, accuracy = 0.16919345057610674
2024-09-27 17:12:31,548 | INFO | With p = 1.0 = 5 / 5, mutating 173 to 279
Idx = 279, accuracy = 0.16919345057610674
2024-09-27 17:12:35,937 | INFO | With p = 1.0 = 5 / 5, mutating 173 to 280
Idx = 280, accuracy = 0.10248635536688902
2024-09-27 17:12:38,768 | INFO | With p = 1.0 = 5 / 5, mutating 173 to 281
Idx = 281, accuracy = 0.10764099454214676
2024-09-27 17:12:42,835 | INFO | With p = 1.0 = 5 / 5, mutating 173 to 282
Idx = 282, accuracy = 0.09126743480897513
2024-09-27 17:12:45,840 | INFO | With p = 1.0 = 5 / 5, mutating 173 to 283
Idx = 283, accuracy = 0.09945421467556094
2024-09-27 17:12:50,779 | INFO | With p = 1.0 = 5 / 5, mutating 173 to 284
Idx = 284, accuracy = 0.1052152819890843
2024-09-27 17:12:56,743 | INFO | With p = 1.0 = 5 / 5, mutating 173 to 285
Idx = 285, accuracy = 0.1052152819890843
2024-09-27 17:13:02,652 | INFO | With p = 1.0 = 5 / 5, mutating 173 to 286
Idx = 286, accuracy = 0.1052152819890843
2024-09-27 17:13:08,564 | INFO | With p = 1.0 = 5 / 5, mutating 173 to 287
Idx = 287, accuracy = 0.1052152819890843
2024-09-27 17:13:13,992 | INFO | With p = 1.0 = 5 / 5, mutating 173 to 288
Idx = 288, accuracy = 0.12765312310491206
2024-09-27 17:13:19,654 | INFO | With p = 1.0 = 5 / 5, mutating 173 to 289
Idx = 289, accuracy = 0.12765312310491206
2024-09-27 17:13:25,317 | INFO | With p = 1.0 = 5 / 5, mutating 173 to 290
Idx = 290, accuracy = 0.09945421467556094
2024-09-27 17:13:34,208 | INFO | With p = 1.0 = 5 / 5, mutating 173 to 291
Idx = 291, accuracy = 0.09945421467556094
2024-09-27 17:13:42,867 | INFO | With p = 1.0 = 5 / 5, mutating 173 to 292
Idx = 292, accuracy = 0.09945421467556094
2024-09-27 17:13:49,236 | INFO | With p = 1.0 = 5 / 5, mutating 173 to 293
Idx = 293, accuracy = 0.09945421467556094
2024-09-27 17:13:55,546 | INFO | With p = 1.0 = 5 / 5, mutating 173 to 294
Idx = 294, accuracy = 0.10248635536688902
2024-09-27 17:14:01,203 | INFO | With p = 1.0 = 5 / 5, mutating 173 to 295
Idx = 295, accuracy = 0.09945421467556094
2024-09-27 17:14:07,523 | INFO | With p = 1.0 = 5 / 5, mutating 173 to 296
Idx = 296, accuracy = 0.09945421467556094
2024-09-27 17:14:18,987 | INFO | With p = 1.0 = 5 / 5, mutating 173 to 297
Idx = 297, accuracy = 0.09945421467556094
2024-09-27 17:14:30,300 | INFO | With p = 1.0 = 5 / 5, mutating 173 to 298
Idx = 298, accuracy = 0.09945421467556094
2024-09-27 17:14:41,657 | INFO | With p = 1.0 = 5 / 5, mutating 173 to 299
Idx = 299, accuracy = 0.09945421467556094
2024-09-27 17:14:52,904 | INFO | With p = 1.0 = 5 / 5, mutating 173 to 300
Idx = 300, accuracy = 0.09945421467556094
2024-09-27 17:15:04,074 | INFO | With p = 1.0 = 5 / 5, mutating 173 to 301
Idx = 301, accuracy = 0.09884778653729533
2024-09-27 17:15:10,354 | INFO | With p = 1.0 = 5 / 5, mutating 173 to 302
Idx = 302, accuracy = 0.1052152819890843
2024-09-27 17:15:18,775 | INFO | With p = 1.0 = 5 / 5, mutating 173 to 303
Idx = 303, accuracy = 0.1052152819890843
2024-09-27 17:15:27,202 | INFO | With p = 1.0 = 5 / 5, mutating 173 to 304
Idx = 304, accuracy = 0.1052152819890843
2024-09-27 17:15:35,584 | INFO | With p = 1.0 = 5 / 5, mutating 173 to 305
Idx = 305, accuracy = 0.09945421467556094
2024-09-27 17:15:41,628 | INFO | With p = 1.0 = 5 / 5, mutating 173 to 306
Idx = 306, accuracy = 0.09945421467556094
2024-09-27 17:15:47,626 | INFO | With p = 1.0 = 5 / 5, mutating 173 to 307
Idx = 307, accuracy = 0.09945421467556094
2024-09-27 17:15:53,591 | INFO | With p = 1.0 = 5 / 5, mutating 173 to 308
Idx = 308, accuracy = 0.09945421467556094
2024-09-27 17:15:59,545 | INFO | With p = 1.0 = 5 / 5, mutating 173 to 309
Idx = 309, accuracy = 0.09581564584596726
2024-09-27 17:16:04,998 | INFO | With p = 1.0 = 5 / 5, mutating 173 to 310
Idx = 310, accuracy = 0.09581564584596726
2024-09-27 17:16:10,689 | INFO | With p = 1.0 = 5 / 5, mutating 173 to 311
Idx = 311, accuracy = 0.09581564584596726
2024-09-27 17:16:16,265 | INFO | With p = 1.0 = 5 / 5, mutating 173 to 312
Idx = 312, accuracy = 0.09945421467556094
2024-09-27 17:16:22,372 | INFO | With p = 1.0 = 5 / 5, mutating 173 to 313
Idx = 313, accuracy = 0.09945421467556094
2024-09-27 17:16:28,473 | INFO | With p = 1.0 = 5 / 5, mutating 173 to 314
Idx = 314, accuracy = 0.09945421467556094
2024-09-27 17:16:34,538 | INFO | With p = 1.0 = 5 / 5, mutating 173 to 315
Idx = 315, accuracy = 0.09975742874469376
2024-09-27 17:16:39,030 | INFO | With p = 1.0 = 5 / 5, mutating 173 to 316
Idx = 316, accuracy = 0.09945421467556094
2024-09-27 17:16:42,715 | INFO | With p = 1.0 = 5 / 5, mutating 173 to 317
Idx = 317, accuracy = 0.09945421467556094
2024-09-27 17:16:47,788 | INFO | With p = 1.0 = 5 / 5, mutating 173 to 318
Idx = 318, accuracy = 0.09945421467556094
2024-09-27 17:16:53,226 | INFO | With p = 1.0 = 5 / 5, mutating 173 to 319
Idx = 319, accuracy = 0.09945421467556094
2024-09-27 17:16:58,921 | INFO | With p = 1.0 = 5 / 5, mutating 173 to 320
Idx = 320, accuracy = 0.09945421467556094
2024-09-27 17:17:04,611 | INFO | With p = 1.0 = 5 / 5, mutating 173 to 321
Idx = 321, accuracy = 0.09945421467556094
2024-09-27 17:17:10,294 | INFO | With p = 1.0 = 5 / 5, mutating 173 to 322
Idx = 322, accuracy = 0.09945421467556094
2024-09-27 17:17:15,961 | INFO | With p = 1.0 = 5 / 5, mutating 173 to 323
Idx = 323, accuracy = 0.09945421467556094
2024-09-27 17:17:21,636 | INFO | With p = 1.0 = 5 / 5, mutating 173 to 324
Idx = 324, accuracy = 0.09945421467556094
2024-09-27 17:17:26,788 | INFO | With p = 1.0 = 5 / 5, mutating 173 to 325
Idx = 325, accuracy = 0.09945421467556094
2024-09-27 17:17:31,920 | INFO | With p = 1.0 = 5 / 5, mutating 173 to 326
Idx = 326, accuracy = 0.08399029714978776
2024-09-27 17:17:37,679 | INFO | With p = 1.0 = 5 / 5, mutating 173 to 327
Idx = 327, accuracy = 0.08399029714978776
2024-09-27 17:17:43,478 | INFO | With p = 1.0 = 5 / 5, mutating 173 to 328
Idx = 328, accuracy = 0.09945421467556094
2024-09-27 17:17:48,051 | INFO | With p = 1.0 = 5 / 5, mutating 173 to 329
Idx = 329, accuracy = 0.10066707095209218
2024-09-27 17:17:52,580 | INFO | With p = 1.0 = 5 / 5, mutating 173 to 330
Idx = 330, accuracy = 0.10097028502122499
2024-09-27 17:17:57,094 | INFO | With p = 1.0 = 5 / 5, mutating 173 to 331
Idx = 331, accuracy = 0.10097028502122499
2024-09-27 17:18:01,583 | INFO | With p = 1.0 = 5 / 5, mutating 173 to 332
Idx = 332, accuracy = 0.10097028502122499
2024-09-27 17:18:06,086 | INFO | With p = 1.0 = 5 / 5, mutating 173 to 333
Idx = 333, accuracy = 0.10794420861127957
2024-09-27 17:18:10,620 | INFO | With p = 1.0 = 5 / 5, mutating 173 to 334
Idx = 334, accuracy = 0.10066707095209218
2024-09-27 17:18:17,588 | INFO | With p = 1.0 = 5 / 5, mutating 173 to 335
Idx = 335, accuracy = 0.07974530018192844
2024-09-27 17:18:23,021 | INFO | With p = 1.0 = 5 / 5, mutating 173 to 336
Idx = 336, accuracy = 0.10430563978168587
2024-09-27 17:18:28,056 | INFO | With p = 1.0 = 5 / 5, mutating 173 to 337
Idx = 337, accuracy = 0.10430563978168587
2024-09-27 17:18:33,056 | INFO | With p = 1.0 = 5 / 5, mutating 173 to 338
Idx = 338, accuracy = 0.10430563978168587
2024-09-27 17:18:38,071 | INFO | With p = 1.0 = 5 / 5, mutating 173 to 339
Idx = 339, accuracy = 0.10430563978168587
2024-09-27 17:18:43,033 | INFO | With p = 1.0 = 5 / 5, mutating 173 to 340
Idx = 340, accuracy = 0.10248635536688902
2024-09-27 17:18:49,340 | INFO | With p = 1.0 = 5 / 5, mutating 173 to 341
Idx = 341, accuracy = 0.0391146149181322
2024-09-27 17:18:54,709 | INFO | With p = 1.0 = 5 / 5, mutating 173 to 342
Idx = 342, accuracy = 0.0391146149181322
2024-09-27 17:19:00,084 | INFO | With p = 1.0 = 5 / 5, mutating 173 to 343
Idx = 343, accuracy = 0.0391146149181322
2024-09-27 17:19:05,431 | INFO | With p = 1.0 = 5 / 5, mutating 173 to 344
Idx = 344, accuracy = 0.8835657974530018
2024-09-27 17:19:08,314 | INFO | With p = 0.2 = 1 / 5, training 344 instead
Idx = 344, accuracy = 0.8829593693147362
2024-09-27 17:19:11,167 | INFO | With p = 0.4 = 2 / 5, mutating 344 to 345
Idx = 345, accuracy = 0.8608247422680413
2024-09-27 17:19:14,046 | INFO | With p = 0.2 = 1 / 5, training 345 instead
Idx = 345, accuracy = 0.8583990297149787
2024-09-27 17:19:16,922 | INFO | With p = 0.4 = 2 / 5, training 344 instead
Idx = 344, accuracy = 0.8132201334141904
2024-09-27 17:19:19,799 | INFO | With p = 0.4 = 2 / 5, training 345 instead
Idx = 345, accuracy = 0.7841115827774409
2024-09-27 17:19:22,709 | INFO | With p = 0.6 = 3 / 5, training 344 instead
Idx = 344, accuracy = 0.8702243784111583
2024-09-27 17:19:25,581 | INFO | With p = 0.8 = 4 / 5, mutating 344 to 346
Idx = 346, accuracy = 0.10491206791995149
2024-09-27 17:19:28,635 | INFO | With p = 0.8 = 4 / 5, mutating 344 to 347
Idx = 347, accuracy = 0.10491206791995149
2024-09-27 17:19:31,784 | INFO | With p = 0.8 = 4 / 5, mutating 344 to 348
Idx = 348, accuracy = 0.09945421467556094
2024-09-27 17:19:34,279 | INFO | With p = 0.8 = 4 / 5, mutating 344 to 349
Idx = 349, accuracy = 0.09945421467556094
2024-09-27 17:19:38,195 | INFO | With p = 0.8 = 4 / 5, mutating 344 to 350
Idx = 350, accuracy = 0.09945421467556094
2024-09-27 17:19:42,013 | INFO | With p = 0.8 = 4 / 5, mutating 344 to 351
Idx = 351, accuracy = 0.8765918738629472
2024-09-27 17:19:44,348 | INFO | With p = 0.2 = 1 / 5, training 351 instead
Idx = 351, accuracy = 0.929047907822923
2024-09-27 17:19:46,691 | INFO | With p = 0.4 = 2 / 5, training 351 instead
Idx = 351, accuracy = 0.9284414796846574
2024-09-27 17:19:49,013 | INFO | With p = 0.6 = 3 / 5, mutating 351 to 352
Idx = 352, accuracy = 0.9241964827167981
2024-09-27 17:19:51,360 | INFO | With p = 0.2 = 1 / 5, training 352 instead
Idx = 352, accuracy = 0.9238932686476653
2024-09-27 17:19:53,702 | INFO | With p = 0.4 = 2 / 5, mutating 352 to 353
Idx = 353, accuracy = 0.8632504548211037
2024-09-27 17:19:56,020 | INFO | With p = 0.4 = 2 / 5, training 352 instead
Idx = 352, accuracy = 0.9329896907216495
2024-09-27 17:19:58,353 | INFO | With p = 0.6 = 3 / 5, mutating 352 to 354
Idx = 354, accuracy = 0.09945421467556094
2024-09-27 17:19:59,902 | INFO | With p = 0.6 = 3 / 5, training 352 instead
Idx = 352, accuracy = 0.09945421467556094
2024-09-27 17:20:01,389 | INFO | With p = 0.2 = 1 / 5, training 353 instead
Idx = 353, accuracy = 0.09945421467556094
2024-09-27 17:20:02,883 | INFO | With p = 0.6 = 3 / 5, mutating 351 to 355
Idx = 355, accuracy = 0.09945421467556094
2024-09-27 17:20:04,634 | INFO | With p = 0.6 = 3 / 5, training 351 instead
Idx = 351, accuracy = 0.09945421467556094
2024-09-27 17:20:06,410 | INFO | With p = 0.8 = 4 / 5, mutating 344 to 356
Idx = 356, accuracy = 0.09945421467556094
2024-09-27 17:20:08,175 | INFO | With p = 0.6 = 3 / 5, mutating 345 to 357
Idx = 357, accuracy = 0.09945421467556094
2024-09-27 17:20:09,791 | INFO | With p = 0.8 = 4 / 5, mutating 344 to 358
Idx = 358, accuracy = 0.09945421467556094
2024-09-27 17:20:11,401 | INFO | With p = 0.8 = 4 / 5, mutating 344 to 359
Idx = 359, accuracy = 0.09945421467556094
2024-09-27 17:20:13,065 | INFO | With p = 0.8 = 4 / 5, mutating 344 to 360
Idx = 360, accuracy = 0.09945421467556094
2024-09-27 17:20:14,658 | INFO | With p = 0.8 = 4 / 5, mutating 344 to 361
Idx = 361, accuracy = 0.09945421467556094
2024-09-27 17:20:16,263 | INFO | With p = 0.8 = 4 / 5, mutating 344 to 362
Idx = 362, accuracy = 0.09945421467556094
2024-09-27 17:20:19,160 | INFO | With p = 0.8 = 4 / 5, mutating 344 to 363
Idx = 363, accuracy = 0.09945421467556094
2024-09-27 17:20:22,633 | INFO | With p = 0.8 = 4 / 5, mutating 344 to 364
Idx = 364, accuracy = 0.09945421467556094
2024-09-27 17:20:26,108 | INFO | With p = 0.8 = 4 / 5, mutating 344 to 365
Idx = 365, accuracy = 0.09945421467556094
2024-09-27 17:20:29,611 | INFO | With p = 0.8 = 4 / 5, mutating 344 to 366
Idx = 366, accuracy = 0.09945421467556094
2024-09-27 17:20:34,628 | INFO | With p = 0.6 = 3 / 5, mutating 345 to 367
Idx = 367, accuracy = 0.10491206791995149
2024-09-27 17:20:39,679 | INFO | With p = 0.8 = 4 / 5, mutating 344 to 368
Idx = 368, accuracy = 0.10491206791995149
2024-09-27 17:20:44,606 | INFO | With p = 0.8 = 4 / 5, mutating 344 to 369
Idx = 369, accuracy = 0.10491206791995149
2024-09-27 17:20:49,525 | INFO | With p = 0.8 = 4 / 5, training 344 instead
Idx = 344, accuracy = 0.10491206791995149
2024-09-27 17:20:54,489 | INFO | With p = 1.0 = 5 / 5, mutating 173 to 370
Idx = 370, accuracy = 0.09945421467556094
2024-09-27 17:20:59,696 | INFO | With p = 1.0 = 5 / 5, mutating 173 to 371
Idx = 371, accuracy = 0.09945421467556094
2024-09-27 17:21:04,887 | INFO | With p = 1.0 = 5 / 5, mutating 173 to 372
Idx = 372, accuracy = 0.09945421467556094
2024-09-27 17:21:09,159 | INFO | With p = 1.0 = 5 / 5, mutating 173 to 373
Idx = 373, accuracy = 0.07307459066100667
2024-09-27 17:21:13,759 | INFO | With p = 1.0 = 5 / 5, mutating 173 to 374
Idx = 374, accuracy = 0.11885991510006064
2024-09-27 17:21:19,354 | INFO | With p = 1.0 = 5 / 5, mutating 173 to 375
Idx = 375, accuracy = 0.09945421467556094
2024-09-27 17:21:24,460 | INFO | With p = 1.0 = 5 / 5, mutating 173 to 376
Idx = 376, accuracy = 0.10278956943602183
2024-09-27 17:21:28,194 | INFO | With p = 1.0 = 5 / 5, mutating 173 to 377
Idx = 377, accuracy = 0.10278956943602183
2024-09-27 17:21:31,613 | INFO | With p = 1.0 = 5 / 5, mutating 173 to 378
Idx = 378, accuracy = 0.12947240751970893
2024-09-27 17:21:34,701 | INFO | With p = 1.0 = 5 / 5, mutating 173 to 379
Idx = 379, accuracy = 0.12947240751970893
2024-09-27 17:21:37,857 | INFO | With p = 1.0 = 5 / 5, mutating 173 to 380
Idx = 380, accuracy = 0.10187992722862341
2024-09-27 17:21:42,365 | INFO | With p = 1.0 = 5 / 5, mutating 173 to 381
Idx = 381, accuracy = 0.10187992722862341
2024-09-27 17:21:46,683 | INFO | With p = 1.0 = 5 / 5, mutating 173 to 382
Idx = 382, accuracy = 0.10187992722862341
2024-09-27 17:21:50,919 | INFO | With p = 1.0 = 5 / 5, mutating 173 to 383
Idx = 383, accuracy = 0.10187992722862341
2024-09-27 17:21:55,534 | INFO | With p = 1.0 = 5 / 5, mutating 173 to 384
Idx = 384, accuracy = 0.10187992722862341
2024-09-27 17:22:00,273 | INFO | With p = 1.0 = 5 / 5, mutating 173 to 385
Idx = 385, accuracy = 0.10187992722862341
2024-09-27 17:22:04,863 | INFO | With p = 1.0 = 5 / 5, mutating 173 to 386
Idx = 386, accuracy = 0.10187992722862341
2024-09-27 17:22:09,480 | INFO | With p = 1.0 = 5 / 5, mutating 173 to 387
Idx = 387, accuracy = 0.10187992722862341
2024-09-27 17:22:14,458 | INFO | With p = 1.0 = 5 / 5, mutating 173 to 388
Idx = 388, accuracy = 0.10187992722862341
2024-09-27 17:22:19,067 | INFO | With p = 1.0 = 5 / 5, mutating 173 to 389
Idx = 389, accuracy = 0.10187992722862341
2024-09-27 17:22:23,343 | INFO | With p = 1.0 = 5 / 5, mutating 173 to 390
Idx = 390, accuracy = 0.10187992722862341
2024-09-27 17:22:27,453 | INFO | With p = 1.0 = 5 / 5, mutating 173 to 391
Idx = 391, accuracy = 0.10187992722862341
2024-09-27 17:22:31,764 | INFO | With p = 1.0 = 5 / 5, mutating 173 to 392
Idx = 392, accuracy = 0.10278956943602183
2024-09-27 17:22:36,326 | INFO | With p = 1.0 = 5 / 5, mutating 173 to 393
Idx = 393, accuracy = 0.10491206791995149
2024-09-27 17:22:41,548 | INFO | With p = 1.0 = 5 / 5, mutating 173 to 394
Idx = 394, accuracy = 0.10278956943602183
2024-09-27 17:22:46,813 | INFO | With p = 1.0 = 5 / 5, mutating 173 to 395
Idx = 395, accuracy = 0.10278956943602183
2024-09-27 17:22:53,899 | INFO | With p = 1.0 = 5 / 5, mutating 173 to 396
Idx = 396, accuracy = 0.10491206791995149
2024-09-27 17:22:59,447 | INFO | With p = 1.0 = 5 / 5, mutating 173 to 397
Idx = 397, accuracy = 0.10278956943602183
2024-09-27 17:23:04,258 | INFO | With p = 1.0 = 5 / 5, mutating 173 to 398
Idx = 398, accuracy = 0.10278956943602183
2024-09-27 17:23:09,000 | INFO | With p = 1.0 = 5 / 5, mutating 173 to 399
Idx = 399, accuracy = 0.10278956943602183
2024-09-27 17:23:13,635 | INFO | With p = 1.0 = 5 / 5, mutating 173 to 400
Idx = 400, accuracy = 0.16040024257125532
2024-09-27 17:23:18,719 | INFO | With p = 1.0 = 5 / 5, mutating 173 to 401
Idx = 401, accuracy = 0.09824135839902971
2024-09-27 17:23:26,112 | INFO | With p = 1.0 = 5 / 5, mutating 173 to 402
Idx = 402, accuracy = 0.16494845360824742
2024-09-27 17:23:32,906 | INFO | With p = 1.0 = 5 / 5, mutating 173 to 403
Idx = 403, accuracy = 0.16494845360824742
2024-09-27 17:23:40,080 | INFO | With p = 1.0 = 5 / 5, mutating 173 to 404
Idx = 404, accuracy = 0.07550030321406913
2024-09-27 17:23:45,273 | INFO | With p = 1.0 = 5 / 5, mutating 173 to 405
Idx = 405, accuracy = 0.07550030321406913
2024-09-27 17:23:50,787 | INFO | With p = 1.0 = 5 / 5, mutating 173 to 406
Idx = 406, accuracy = 0.10976349302607641
2024-09-27 17:23:57,084 | INFO | With p = 1.0 = 5 / 5, mutating 173 to 407
Idx = 407, accuracy = 0.10976349302607641
2024-09-27 17:24:03,483 | INFO | With p = 1.0 = 5 / 5, mutating 173 to 408
Idx = 408, accuracy = 0.10976349302607641
2024-09-27 17:24:10,779 | INFO | With p = 1.0 = 5 / 5, mutating 173 to 409
Idx = 409, accuracy = 0.10976349302607641
2024-09-27 17:24:17,531 | INFO | With p = 1.0 = 5 / 5, mutating 173 to 410
Idx = 410, accuracy = 0.10976349302607641
2024-09-27 17:24:24,867 | INFO | With p = 0.6 = 3 / 5, training 345 instead
Idx = 345, accuracy = 0.10976349302607641
2024-09-27 17:24:31,890 | INFO | With p = 1.0 = 5 / 5, mutating 173 to 411
Idx = 411, accuracy = 0.09187386294724076
2024-09-27 17:24:38,483 | INFO | With p = 1.0 = 5 / 5, mutating 173 to 412
Idx = 412, accuracy = 0.09096422073984232
2024-09-27 17:24:46,144 | INFO | With p = 1.0 = 5 / 5, mutating 173 to 413
Idx = 413, accuracy = 0.10278956943602183
2024-09-27 17:24:53,273 | INFO | With p = 1.0 = 5 / 5, mutating 173 to 414
Idx = 414, accuracy = 0.09945421467556094
2024-09-27 17:24:59,175 | INFO | With p = 1.0 = 5 / 5, mutating 173 to 415
Idx = 415, accuracy = 0.10278956943602183
2024-09-27 17:25:07,357 | INFO | With p = 1.0 = 5 / 5, mutating 173 to 416
Idx = 416, accuracy = 0.10278956943602183
2024-09-27 17:25:13,375 | INFO | With p = 1.0 = 5 / 5, mutating 173 to 417
Idx = 417, accuracy = 0.09945421467556094
2024-09-27 17:25:19,140 | INFO | With p = 1.0 = 5 / 5, mutating 173 to 418
Idx = 418, accuracy = 0.09945421467556094
2024-09-27 17:25:24,940 | INFO | With p = 1.0 = 5 / 5, mutating 173 to 419
Idx = 419, accuracy = 0.09945421467556094
2024-09-27 17:25:30,697 | INFO | With p = 1.0 = 5 / 5, mutating 173 to 420
Idx = 420, accuracy = 0.09945421467556094
2024-09-27 17:25:36,522 | INFO | With p = 1.0 = 5 / 5, mutating 173 to 421
Idx = 421, accuracy = 0.09945421467556094
2024-09-27 17:25:42,435 | INFO | With p = 1.0 = 5 / 5, mutating 173 to 422
Idx = 422, accuracy = 0.09945421467556094
2024-09-27 17:25:48,295 | INFO | With p = 1.0 = 5 / 5, mutating 173 to 423
Idx = 423, accuracy = 0.10278956943602183
2024-09-27 17:25:54,154 | INFO | With p = 1.0 = 5 / 5, mutating 173 to 424
Idx = 424, accuracy = 0.12583383869011522
2024-09-27 17:25:59,905 | INFO | With p = 1.0 = 5 / 5, mutating 173 to 425
Idx = 425, accuracy = 0.12340812613705276
2024-09-27 17:26:05,719 | INFO | With p = 1.0 = 5 / 5, mutating 173 to 426
Idx = 426, accuracy = 0.18041237113402062
2024-09-27 17:26:11,472 | INFO | With p = 1.0 = 5 / 5, mutating 173 to 427
Idx = 427, accuracy = 0.19284414796846575
2024-09-27 17:26:17,177 | INFO | With p = 1.0 = 5 / 5, mutating 173 to 428
Idx = 428, accuracy = 0.19739235900545785
2024-09-27 17:26:22,891 | INFO | With p = 1.0 = 5 / 5, mutating 173 to 429
Idx = 429, accuracy = 0.12158884172225591
2024-09-27 17:26:28,135 | INFO | With p = 1.0 = 5 / 5, mutating 173 to 430
Idx = 430, accuracy = 0.20891449363250456
2024-09-27 17:26:33,048 | INFO | With p = 1.0 = 5 / 5, mutating 173 to 431
Idx = 431, accuracy = 0.15342631898120074
2024-09-27 17:26:37,917 | INFO | With p = 1.0 = 5 / 5, mutating 173 to 432
Idx = 432, accuracy = 0.09945421467556094
2024-09-27 17:26:42,610 | INFO | With p = 1.0 = 5 / 5, mutating 173 to 433
Idx = 433, accuracy = 0.09945421467556094
2024-09-27 17:26:46,972 | INFO | With p = 1.0 = 5 / 5, mutating 173 to 434
Idx = 434, accuracy = 0.09126743480897513