diff --git a/exps/algos/BOHB.py b/exps/algos/BOHB.py
index ad38dae..c846e0b 100644
--- a/exps/algos/BOHB.py
+++ b/exps/algos/BOHB.py
@@ -62,11 +62,12 @@ class MyWorker(Worker):
 
   def compute(self, config, budget, **kwargs):
     structure = self.convert_func( config )
-    reward    = train_and_eval(structure, self.nas_bench, None)
+    reward, time_cost = train_and_eval(structure, self.nas_bench, None)
+    import pdb; pdb.set_trace()
     self.test_time += 1
     return ({
             'loss': float(100-reward),
-            'info': None})
+            'info': time_cost})
 
 
 def main(xargs, nas_bench):
@@ -121,7 +122,7 @@ def main(xargs, nas_bench):
 
   bohb = BOHB(configspace=cs,
             run_id=hb_run_id,
-            eta=3, min_budget=3, max_budget=108,
+            eta=3, min_budget=3, max_budget=xargs.time_budget,
             nameserver=ns_host,
             nameserver_port=ns_port,
             num_samples=xargs.num_samples,
@@ -130,6 +131,7 @@ def main(xargs, nas_bench):
   #          optimization_strategy=xargs.strategy, num_samples=xargs.num_samples,
   
   results = bohb.run(xargs.n_iters, min_n_workers=num_workers)
+  import pdb; pdb.set_trace()
 
   bohb.shutdown(shutdown_workers=True)
   NS.shutdown()
@@ -160,9 +162,10 @@ if __name__ == '__main__':
   parser.add_argument('--max_nodes',          type=int,   help='The maximum number of nodes.')
   parser.add_argument('--channel',            type=int,   help='The number of channels.')
   parser.add_argument('--num_cells',          type=int,   help='The number of cells in one stage.')
+  parser.add_argument('--time_budget',        type=int,   help='The total time cost budge for searching (in seconds).')
   # BOHB
   parser.add_argument('--strategy', default="sampling", type=str, nargs='?', help='optimization strategy for the acquisition function')
-  parser.add_argument('--min_bandwidth', default=.3, type=float, nargs='?', help='minimum bandwidth for KDE')
+  parser.add_argument('--min_bandwidth',    default=.3, type=float, nargs='?', help='minimum bandwidth for KDE')
   parser.add_argument('--num_samples',      default=64, type=int, nargs='?', help='number of samples for the acquisition function')
   parser.add_argument('--random_fraction',  default=.33, type=float, nargs='?', help='fraction of random configurations')
   parser.add_argument('--bandwidth_factor', default=3, type=int, nargs='?', help='factor multiplied to the bandwidth')
diff --git a/exps/algos/RANDOM-NAS.py b/exps/algos/RANDOM-NAS.py
index 3f4ac9f..dd9653c 100644
--- a/exps/algos/RANDOM-NAS.py
+++ b/exps/algos/RANDOM-NAS.py
@@ -82,6 +82,16 @@ def valid_func(xloader, network, criterion):
   return arch_losses.avg, arch_top1.avg, arch_top5.avg
 
 
+def search_find_best(valid_loader, network, criterion, select_num):
+  best_arch, best_acc = None, -1
+  for iarch in range(select_num):
+    arch = network.module.random_genotype( True )
+    valid_a_loss, valid_a_top1, valid_a_top5  = valid_func(valid_loader, network, criterion)
+    if best_arch is None or best_acc < valid_a_top1:
+      best_arch, best_acc = arch, valid_a_top1
+  return best_arch
+
+
 def main(xargs):
   assert torch.cuda.is_available(), 'CUDA is not available.'
   torch.backends.cudnn.enabled   = True
@@ -143,6 +153,7 @@ def main(xargs):
     last_info   = torch.load(last_info)
     start_epoch = last_info['epoch']
     checkpoint  = torch.load(last_info['last_checkpoint'])
+    genotypes   = checkpoint['genotypes']
     valid_accuracies = checkpoint['valid_accuracies']
     search_model.load_state_dict( checkpoint['search_model'] )
     w_scheduler.load_state_dict ( checkpoint['w_scheduler'] )
@@ -150,7 +161,7 @@ def main(xargs):
     logger.log("=> loading checkpoint of the last-info '{:}' start with {:}-th epoch.".format(last_info, start_epoch))
   else:
     logger.log("=> do not find the last-info file : {:}".format(last_info))
-    start_epoch, valid_accuracies = 0, {'best': -1}
+    start_epoch, valid_accuracies, genotypes = 0, {'best': -1}, {}
 
   # start training
   start_time, search_time, epoch_time, total_epoch = time.time(), AverageMeter(), AverageMeter(), config.epochs + config.warmup
@@ -160,11 +171,14 @@ def main(xargs):
     epoch_str = '{:03d}-{:03d}'.format(epoch, total_epoch)
     logger.log('\n[Search the {:}-th epoch] {:}, LR={:}'.format(epoch_str, need_time, min(w_scheduler.get_lr())))
 
+    # selected_arch = search_find_best(valid_loader, network, criterion, xargs.select_num)
     search_w_loss, search_w_top1, search_w_top5 = search_func(search_loader, network, criterion, w_scheduler, w_optimizer, epoch_str, xargs.print_freq, logger)
     search_time.update(time.time() - start_time)
     logger.log('[{:}] searching : loss={:.2f}, accuracy@1={:.2f}%, accuracy@5={:.2f}%, time-cost={:.1f} s'.format(epoch_str, search_w_loss, search_w_top1, search_w_top5, search_time.sum))
     valid_a_loss , valid_a_top1 , valid_a_top5  = valid_func(valid_loader, network, criterion)
     logger.log('[{:}] evaluate  : loss={:.2f}, accuracy@1={:.2f}%, accuracy@5={:.2f}%'.format(epoch_str, valid_a_loss, valid_a_top1, valid_a_top5))
+    cur_arch = search_find_best(valid_loader, network, criterion, xargs.select_num)
+    genotypes[epoch] = cur_arch
     # check the best accuracy
     valid_accuracies[epoch] = valid_a_top1
     if valid_a_top1 > valid_accuracies['best']:
@@ -178,6 +192,7 @@ def main(xargs):
                 'search_model': search_model.state_dict(),
                 'w_optimizer' : w_optimizer.state_dict(),
                 'w_scheduler' : w_scheduler.state_dict(),
+                'genotypes'   : genotypes,
                 'valid_accuracies' : valid_accuracies},
                 model_base_path, logger)
     last_info = save_checkpoint({
@@ -188,6 +203,7 @@ def main(xargs):
     if find_best:
       logger.log('<<<--->>> The {:}-th epoch : find the highest validation accuracy : {:.2f}%.'.format(epoch_str, valid_a_top1))
       copy_checkpoint(model_base_path, model_best_path, logger)
+    if api is not None: logger.log('{:}'.format(api.query_by_arch( genotypes[epoch] )))
     # measure elapsed time
     epoch_time.update(time.time() - start_time)
     start_time = time.time()
@@ -202,7 +218,6 @@ def main(xargs):
     logger.log('final evaluation [{:02d}/{:02d}] : {:} : accuracy={:.2f}%, loss={:.3f}'.format(iarch, xargs.select_num, arch, valid_a_top1, valid_a_loss))
     if best_arch is None or best_acc < valid_a_top1:
       best_arch, best_acc = arch, valid_a_top1
-
   search_time.update(time.time() - start_time)
   logger.log('RANDOM-NAS finds the best one : {:} with accuracy={:.2f}%, with {:.1f} s.'.format(best_arch, best_acc, search_time.sum))
   if api is not None: logger.log('{:}'.format( api.query_by_arch(best_arch) ))
diff --git a/exps/algos/reinforce.py b/exps/algos/reinforce.py
index 389a0ce..92a941e 100644
--- a/exps/algos/reinforce.py
+++ b/exps/algos/reinforce.py
@@ -17,7 +17,7 @@ from datasets     import get_datasets, SearchDataset
 from procedures   import prepare_seed, prepare_logger, save_checkpoint, copy_checkpoint, get_optim_scheduler
 from utils        import get_model_infos, obtain_accuracy
 from log_utils    import AverageMeter, time_string, convert_secs2time
-from nas_102_api  import NASBench102API
+from nas_102_api  import NASBench102API as API
 from models       import CellStructure, get_search_spaces
 from R_EA import train_and_eval
 
@@ -132,10 +132,18 @@ def main(xargs, nas_bench):
 
   # REINFORCE
   # attempts = 0
-  for istep in range(xargs.RL_steps):
+  logger.log('Will start searching with time budget of {:} s.'.format(xargs.time_budget))
+  total_steps, total_costs = 0, 0
+  #for istep in range(xargs.RL_steps):
+  while total_costs < xargs.time_budget:
+    start_time = time.time()
     log_prob, action = select_action( policy )
     arch   = policy.generate_arch( action )
-    reward = train_and_eval(arch, nas_bench, extra_info)
+    reward, cost_time = train_and_eval(arch, nas_bench, extra_info)
+    # accumulate time
+    if total_costs + cost_time < xargs.time_budget:
+      total_costs += cost_time
+    else: break
 
     baseline.update(reward)
     # calculate loss
@@ -143,13 +151,15 @@ def main(xargs, nas_bench):
     optimizer.zero_grad()
     policy_loss.backward()
     optimizer.step()
-
-    logger.log('step [{:3d}/{:3d}] : average-reward={:.3f} : policy_loss={:.4f} : {:}'.format(istep, xargs.RL_steps, baseline.value(), policy_loss.item(), policy.genotype()))
+    # accumulate time
+    total_costs += time.time() - start_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('')
+    #logger.log('')
 
   best_arch = policy.genotype()
-
+  logger.log('REINFORCE finish with {:} steps and {:.1f} s.'.format(total_steps, total_costs))
   info = nas_bench.query_by_arch( best_arch )
   if info is None: logger.log('Did not find this architecture : {:}.'.format(best_arch))
   else           : logger.log('{:}'.format(info))
@@ -169,8 +179,9 @@ if __name__ == '__main__':
   parser.add_argument('--channel',            type=int,   help='The number of channels.')
   parser.add_argument('--num_cells',          type=int,   help='The number of cells in one stage.')
   parser.add_argument('--learning_rate',      type=float, help='The learning rate for REINFORCE.')
-  parser.add_argument('--RL_steps',           type=int,   help='The steps for REINFORCE.')
+  #parser.add_argument('--RL_steps',           type=int,   help='The steps for REINFORCE.')
   parser.add_argument('--EMA_momentum',       type=float, help='The momentum value for EMA.')
+  parser.add_argument('--time_budget',        type=int,   help='The total time cost budge for searching (in seconds).')
   # log
   parser.add_argument('--workers',            type=int,   default=2,    help='number of data loading workers (default: 2)')
   parser.add_argument('--save_dir',           type=str,   help='Folder to save checkpoints and log.')
@@ -183,7 +194,7 @@ if __name__ == '__main__':
     nas_bench = None
   else:
     print ('{:} build NAS-Benchmark-API from {:}'.format(time_string(), args.arch_nas_dataset))
-    nas_bench = AANASBenchAPI(args.arch_nas_dataset)
+    nas_bench = API(args.arch_nas_dataset)
   if args.rand_seed < 0:
     save_dir, all_indexes, num = None, [], 500
     for i in range(num):
diff --git a/scripts-search/algos/BOHB.sh b/scripts-search/algos/BOHB.sh
index c08f5c6..59ea98d 100644
--- a/scripts-search/algos/BOHB.sh
+++ b/scripts-search/algos/BOHB.sh
@@ -34,5 +34,6 @@ OMP_NUM_THREADS=4 python ./exps/algos/BOHB.py \
 	--dataset ${dataset} --data_path ${data_path} \
 	--search_space_name ${space} \
 	--arch_nas_dataset ${TORCH_HOME}/NAS-Bench-102-v1_0-e61699.pth \
-	--n_iters 6 --num_samples 3 \
+	--time_budget 12000 \
+	--n_iters 100 --num_samples 4 --random_fraction 0 \
 	--workers 4 --print_freq 200 --rand_seed ${seed}
diff --git a/scripts-search/algos/REINFORCE.sh b/scripts-search/algos/REINFORCE.sh
index add5ac7..1e78af2 100644
--- a/scripts-search/algos/REINFORCE.sh
+++ b/scripts-search/algos/REINFORCE.sh
@@ -34,5 +34,6 @@ OMP_NUM_THREADS=4 python ./exps/algos/reinforce.py \
 	--dataset ${dataset} --data_path ${data_path} \
 	--search_space_name ${space} \
 	--arch_nas_dataset ${TORCH_HOME}/NAS-Bench-102-v1_0-e61699.pth \
-	--learning_rate 0.001 --RL_steps 100 --EMA_momentum 0.9 \
+	--time_budget 12000 \
+	--learning_rate 0.001 --EMA_momentum 0.9 \
 	--workers 4 --print_freq 200 --rand_seed ${seed}