diff --git a/exps/algos-v2/random_wo_share.py b/exps/algos-v2/random_wo_share.py
index 774dfd4..5fd1d73 100644
--- a/exps/algos-v2/random_wo_share.py
+++ b/exps/algos-v2/random_wo_share.py
@@ -4,6 +4,8 @@
 # Random Search for Hyper-Parameter Optimization, JMLR 2012 ##################
 ##############################################################################
 # python ./exps/algos-v2/random_wo_share.py --dataset cifar10 --search_space tss
+# python ./exps/algos-v2/random_wo_share.py --dataset cifar100 --search_space tss
+# python ./exps/algos-v2/random_wo_share.py --dataset ImageNet16-120 --search_space tss
 ##############################################################################
 import os, sys, time, glob, random, argparse
 import numpy as np, collections
@@ -20,7 +22,7 @@ from utils        import get_model_infos, obtain_accuracy
 from log_utils    import AverageMeter, time_string, convert_secs2time
 from models       import get_search_spaces
 from nas_201_api  import NASBench201API, NASBench301API
-from .regularized_ea import random_topology_func, random_size_func
+from regularized_ea import random_topology_func, random_size_func
 
 
 def main(xargs, api):
@@ -28,16 +30,18 @@ def main(xargs, api):
   prepare_seed(xargs.rand_seed)
   logger = prepare_logger(args)
 
+  logger.log('{:} use api : {:}'.format(time_string(), api))
+  api.reset_time()
+
   search_space = get_search_spaces(xargs.search_space, 'nas-bench-301')
   if xargs.search_space == 'tss':
     random_arch = random_topology_func(search_space)
   else:
     random_arch = random_size_func(search_space)
 
-  x_start_time = time.time()
-  logger.log('{:} use nas_bench : {:}'.format(time_string(), nas_bench))
   best_arch, best_acc, total_time_cost, history = None, -1, [], []
-  while total_time_cost[-1] < xargs.time_budget:
+  current_best_index = []
+  while len(total_time_cost) == 0 or total_time_cost[-1] < xargs.time_budget:
     arch = random_arch()
     accuracy, _, _, total_cost = api.simulate_train_eval(arch, xargs.dataset, '12')
     total_time_cost.append(total_cost)
@@ -45,13 +49,14 @@ def main(xargs, api):
     if best_arch is None or best_acc < accuracy:
       best_acc, best_arch = accuracy, arch
     logger.log('[{:03d}] : {:} : accuracy = {:.2f}%'.format(len(history), arch, accuracy))
-  logger.log('{:} best arch is {:}, accuracy = {:.2f}%, visit {:} archs with {:.1f} s (real-cost = {:.3f} s).'.format(time_string(), best_arch, best_acc, len(history), total_time_cost, time.time()-x_start_time))
+    current_best_index.append(api.query_index_by_arch(best_arch))
+  logger.log('{:} best arch is {:}, accuracy = {:.2f}%, visit {:} archs with {:.1f} s.'.format(time_string(), best_arch, best_acc, len(history), total_time_cost[-1]))
   
   info = api.query_info_str_by_arch(best_arch, '200' if xargs.search_space == 'tss' else '90')
   logger.log('{:}'.format(info))
   logger.log('-'*100)
   logger.close()
-  return logger.log_dir, total_time_cost, history
+  return logger.log_dir, current_best_index, total_time_cost
 
 
 if __name__ == '__main__':
@@ -62,7 +67,7 @@ if __name__ == '__main__':
   parser.add_argument('--time_budget',        type=int,   default=20000, help='The total time cost budge for searching (in seconds).')
   parser.add_argument('--loops_if_rand',      type=int,   default=500,   help='The total runs for evaluation.')
   # log
-  parser.add_argument('--save_dir',           type=str,   help='Folder to save checkpoints and log.')
+  parser.add_argument('--save_dir',           type=str,   default='./output/search', help='Folder to save checkpoints and log.')
   parser.add_argument('--rand_seed',          type=int,   default=-1,    help='manual seed')
   args = parser.parse_args()
   
@@ -77,7 +82,7 @@ if __name__ == '__main__':
   print('save-dir : {:}'.format(args.save_dir))
 
   if args.rand_seed < 0:
-    save_dir, all_info = None, {}
+    save_dir, all_info = None, collections.OrderedDict()
     for i in range(args.loops_if_rand):
       print ('{:} : {:03d}/{:03d}'.format(time_string(), i, args.loops_if_rand))
       args.rand_seed = random.randint(1, 100000)
diff --git a/exps/algos-v2/regularized_ea.py b/exps/algos-v2/regularized_ea.py
index 4e0a3bd..845bd28 100644
--- a/exps/algos-v2/regularized_ea.py
+++ b/exps/algos-v2/regularized_ea.py
@@ -155,7 +155,7 @@ def regularized_evolution(cycles, population_size, sample_size, time_budget, ran
   population = collections.deque()
   api.reset_time()
   history, total_time_cost = [], []  # Not used by the algorithm, only used to report results.
-
+  current_best_index = []
   # Initialize the population with random models.
   while len(population) < population_size:
     model = Model()
@@ -163,8 +163,9 @@ def regularized_evolution(cycles, population_size, sample_size, time_budget, ran
     model.accuracy, _, _, total_cost = api.simulate_train_eval(model.arch, dataset, '12')
     # Append the info
     population.append(model)
-    history.append(model)
+    history.append((model.accuracy, model.arch))
     total_time_cost.append(total_cost)
+    current_best_index.append(api.query_index_by_arch(max(history, key=lambda x: x[0])[1]))
 
   # Carry out evolution in cycles. Each cycle produces a model and removes another.
   while total_time_cost[-1] < time_budget:
@@ -183,15 +184,16 @@ def regularized_evolution(cycles, population_size, sample_size, time_budget, ran
     # Create the child model and store it.
     child = Model()
     child.arch = mutate_arch(parent.arch)
-    child.accuracy, _, _, total_cost = api.simulate_train_eval(model.arch, dataset, '12')
+    child.accuracy, _, _, total_cost = api.simulate_train_eval(child.arch, dataset, '12')
     # Append the info
     population.append(child)
-    history.append(child)
+    history.append((child.accuracy, child.arch))
+    current_best_index.append(api.query_index_by_arch(max(history, key=lambda x: x[0])[1]))
     total_time_cost.append(total_cost)
 
     # Remove the oldest model.
     population.popleft()
-  return history, total_time_cost
+  return history, current_best_index, total_time_cost
 
 
 def main(xargs, api):
@@ -210,7 +212,7 @@ def main(xargs, api):
   x_start_time = time.time()
   logger.log('{:} use api : {:}'.format(time_string(), api))
   logger.log('-'*30 + ' start searching with the time budget of {:} s'.format(xargs.time_budget))
-  history, total_times = regularized_evolution(xargs.ea_cycles, xargs.ea_population, xargs.ea_sample_size, xargs.time_budget, random_arch, mutate_arch, api, xargs.dataset)
+  history, current_best_index, total_times = regularized_evolution(xargs.ea_cycles, xargs.ea_population, xargs.ea_sample_size, xargs.time_budget, random_arch, mutate_arch, api, xargs.dataset)
   logger.log('{:} regularized_evolution finish with history of {:} arch with {:.1f} s (real-cost={:.2f} s).'.format(time_string(), len(history), total_times[-1], time.time()-x_start_time))
   best_arch = max(history, key=lambda i: i.accuracy)
   best_arch = best_arch.arch
@@ -220,7 +222,7 @@ def main(xargs, api):
   logger.log('{:}'.format(info))
   logger.log('-'*100)
   logger.close()
-  return logger.log_dir, [api.query_index_by_arch(x.arch) for x in history], total_times
+  return logger.log_dir, current_best_index, total_times
 
 
 if __name__ == '__main__':
@@ -249,7 +251,7 @@ if __name__ == '__main__':
   print('save-dir : {:}'.format(args.save_dir))
 
   if args.rand_seed < 0:
-    save_dir, all_info = None, {}
+    save_dir, all_info = None, collections.OrderedDict()
     for i in range(args.loops_if_rand):
       print ('{:} : {:03d}/{:03d}'.format(time_string(), i, args.loops_if_rand))
       args.rand_seed = random.randint(1, 100000)
diff --git a/exps/algos-v2/reinforce.py b/exps/algos-v2/reinforce.py
index 400f1ef..11babe4 100644
--- a/exps/algos-v2/reinforce.py
+++ b/exps/algos-v2/reinforce.py
@@ -145,6 +145,7 @@ def main(xargs, api):
   x_start_time = time.time()
   logger.log('Will start searching with time budget of {:} s.'.format(xargs.time_budget))
   total_steps, total_costs, trace = 0, [], []
+  current_best_index = []
   while len(total_costs) == 0 or total_costs[-1] < xargs.time_budget:
     start_time = time.time()
     log_prob, action = select_action( policy )
@@ -162,9 +163,8 @@ def main(xargs, api):
     # accumulate time
     total_steps += 1
     logger.log('step [{:3d}] : average-reward={:.3f} : policy_loss={:.4f} : {:}'.format(total_steps, baseline.value(), policy_loss.item(), policy.genotype()))
-    #logger.log('----> {:}'.format(policy.arch_parameters))
-    #logger.log('')
-
+    # to analyze
+    current_best_index.append(api.query_index_by_arch(max(trace, key=lambda x: x[0])[1]))
   # best_arch = policy.genotype() # first version
   best_arch = max(trace, key=lambda x: x[0])[1]
   logger.log('REINFORCE finish with {:} steps and {:.1f} s (real cost={:.3f}).'.format(total_steps, total_costs[-1], time.time()-x_start_time))
@@ -173,7 +173,7 @@ def main(xargs, api):
   logger.log('-'*100)
   logger.close()
 
-  return logger.log_dir, [api.query_index_by_arch(x[1]) for x in trace], total_costs
+  return logger.log_dir, current_best_index, total_costs
 
 
 if __name__ == '__main__':
@@ -203,7 +203,7 @@ if __name__ == '__main__':
   print('save-dir : {:}'.format(args.save_dir))
 
   if args.rand_seed < 0:
-    save_dir, all_info = None, {}
+    save_dir, all_info = None, collections.OrderedDict()
     for i in range(args.loops_if_rand):
       print ('{:} : {:03d}/{:03d}'.format(time_string(), i, args.loops_if_rand))
       args.rand_seed = random.randint(1, 100000)
diff --git a/exps/algos-v2/run-all.sh b/exps/algos-v2/run-all.sh
index 3f2f01d..41a907b 100644
--- a/exps/algos-v2/run-all.sh
+++ b/exps/algos-v2/run-all.sh
@@ -13,5 +13,6 @@ do
   do
     python ./exps/algos-v2/reinforce.py --dataset ${dataset} --search_space ${search_space} --learning_rate 0.001
     python ./exps/algos-v2/regularized_ea.py --dataset ${dataset} --search_space ${search_space} --ea_cycles 200 --ea_population 10 --ea_sample_size 3
+    python ./exps/algos-v2/random_wo_share.py --dataset ${dataset} --search_space ${search_space}
   done
 done
diff --git a/exps/experimental/vis-bench-algos.py b/exps/experimental/vis-bench-algos.py
index f0a4b1b..2cc1f51 100644
--- a/exps/experimental/vis-bench-algos.py
+++ b/exps/experimental/vis-bench-algos.py
@@ -3,7 +3,7 @@
 ###############################################################
 # Copyright (c) Xuanyi Dong [GitHub D-X-Y], 2020.06           #
 ###############################################################
-# Usage: python exps/experimental/vis-bench-algos.py 
+# Usage: python exps/experimental/vis-bench-algos.py          #
 ###############################################################
 import os, sys, time, torch, argparse
 import numpy as np
@@ -30,6 +30,7 @@ def fetch_data(root_dir='./output/search', search_space='tss', dataset=None):
   alg2name, alg2path = OrderedDict(), OrderedDict()
   alg2name['REA'] = 'R-EA-SS3'
   alg2name['REINFORCE'] = 'REINFORCE-0.001'
+  # alg2name['RANDOM'] = 'RANDOM'
   for alg, name in alg2name.items():
     alg2path[alg] = os.path.join(ss_dir, dataset, name, 'results.pth')
     assert os.path.isfile(alg2path[alg])
@@ -62,14 +63,15 @@ def visualize_curve(api, vis_save_dir, search_space, max_time):
   vis_save_dir = vis_save_dir.resolve()
   vis_save_dir.mkdir(parents=True, exist_ok=True)
 
-  dpi, width, height = 250, 4700, 1500
+  dpi, width, height = 250, 5100, 1500
   figsize = width / float(dpi), height / float(dpi)
   LabelSize, LegendFontsize = 14, 14
 
   def sub_plot_fn(ax, dataset):
     alg2data = fetch_data(search_space=search_space, dataset=dataset)
     alg2accuracies = OrderedDict()
-    time_tickets = [float(i) / 100 * max_time for i in range(100)]
+    total_tickets = 150
+    time_tickets = [float(i) / total_tickets * max_time for i in range(total_tickets)]
     colors = ['b', 'g', 'c', 'm', 'y']
     for idx, (alg, data) in enumerate(alg2data.items()):
       print('plot alg : {:}'.format(alg))
@@ -78,7 +80,10 @@ def visualize_curve(api, vis_save_dir, search_space, max_time):
         accuracy = query_performance(api, data, dataset, ticket)
         accuracies.append(accuracy)
       alg2accuracies[alg] = accuracies
-      ax.plot(time_tickets, accuracies, c=colors[idx], label='{:}'.format(alg))
+      ax.plot([x/100 for x in time_tickets], accuracies, c=colors[idx], label='{:}'.format(alg))
+      ax.set_xlabel('Estimated wall-clock time (1e2 seconds)', fontsize=LabelSize)
+      ax.set_ylabel('Test accuracy on {:}'.format(dataset), fontsize=LabelSize)
+      ax.set_title('Searching results on {:}'.format(dataset), fontsize=LabelSize+4)
     ax.legend(loc=4, fontsize=LegendFontsize)
 
   fig, axs = plt.subplots(1, 3, figsize=figsize)
@@ -104,4 +109,3 @@ if __name__ == '__main__':
   visualize_curve(api201, save_dir, 'tss', args.max_time)
   api301 = NASBench301API(verbose=False)
   visualize_curve(api301, save_dir, 'sss', args.max_time)
-