diff --git a/package/samplers/auto_sampler/sampler.py b/package/samplers/auto_sampler/sampler.py
index cab1d7d2..29159b2a 100644
--- a/package/samplers/auto_sampler/sampler.py
+++ b/package/samplers/auto_sampler/sampler.py
@@ -96,28 +96,23 @@ def _include_conditional_param(self, study: Study) -> bool:
             return False
 
         param_key = set(trials[0].params)
-        for t in trials:
-            if param_key != set(t.params):
-                return True
-
-        return False
+        return any(param_key != set(t.params) for t in trials)
 
     def _determine_multi_objective_sampler(
         self, study: Study, trial: FrozenTrial, search_space: dict[str, BaseDistribution]
-    ) -> None:
+    ) -> BaseSampler:
         if isinstance(self._sampler, (NSGAIISampler, NSGAIIISampler)):
-            return
+            return self._sampler
 
         seed = self._rng.rng.randint(MAXINT32)
         if not isinstance(self._sampler, TPESampler):
-            self._sampler = TPESampler(
+            return TPESampler(
                 seed=seed,
                 multivariate=True,
                 warn_independent_sampling=False,
                 constraints_func=self._constraints_func,
                 constant_liar=True,
             )
-            return
 
         complete_trials = study.get_trials(deepcopy=False, states=(TrialState.COMPLETE,))
         complete_trials.sort(key=lambda trial: trial.datetime_complete)
@@ -128,13 +123,15 @@ def _determine_multi_objective_sampler(
                 else NSGAIIISampler
             )
             # Use NSGA-II/III if len(complete_trials) <= _N_COMPLETE_TRIALS_FOR_NSGA.
-            self._sampler = nsga_sampler_cls(constraints_func=self._constraints_func, seed=seed)
+            return nsga_sampler_cls(constraints_func=self._constraints_func, seed=seed)
+
+        return self._sampler
 
     def _determine_single_objective_sampler(
         self, study: Study, trial: FrozenTrial, search_space: dict[str, BaseDistribution]
-    ) -> None:
+    ) -> BaseSampler | None:
         if isinstance(self._sampler, TPESampler):
-            return
+            return self._sampler
 
         seed = self._rng.rng.randint(MAXINT32)
         if (
@@ -145,14 +142,13 @@ def _determine_single_objective_sampler(
             # NOTE(nabenabe): The statement above is always true for Trial#1.
             # Use ``TPESampler`` if search space includes conditional or categorical parameters.
             # TBD: group=True?
-            self._sampler = TPESampler(
+            return TPESampler(
                 seed=seed,
                 multivariate=True,
                 warn_independent_sampling=False,
                 constraints_func=self._constraints_func,
                 constant_liar=True,
             )
-            return
 
         complete_trials = study.get_trials(deepcopy=False, states=(TrialState.COMPLETE,))
         complete_trials.sort(key=lambda trial: trial.datetime_complete)
@@ -160,27 +156,27 @@ def _determine_single_objective_sampler(
             # Use ``GPSampler`` if search space is numerical and
             # len(complete_trials) < _N_COMPLETE_TRIALS_FOR_CMAES.
             if not isinstance(self._sampler, GPSampler):
-                self._sampler = GPSampler(seed=seed)
-            return
-
-        if not isinstance(self._sampler, CmaEsSampler):
+                return GPSampler(seed=seed)
+        elif not isinstance(self._sampler, CmaEsSampler):
             # Use ``CmaEsSampler`` if search space is numerical and
             # len(complete_trials) > _N_COMPLETE_TRIALS_FOR_CMAES.
             # Warm start CMA-ES with the first _N_COMPLETE_TRIALS_FOR_CMAES complete trials.
             warm_start_trials = complete_trials[: self._N_COMPLETE_TRIALS_FOR_CMAES]
             # NOTE(nabenabe): ``CmaEsSampler`` internally falls back to ``RandomSampler`` for
             # 1D problems.
-            self._sampler = CmaEsSampler(
+            return CmaEsSampler(
                 seed=seed, source_trials=warm_start_trials, warn_independent_sampling=False
             )
 
+        return self._sampler
+
     def _determine_sampler(
         self, study: Study, trial: FrozenTrial, search_space: dict[str, BaseDistribution]
     ) -> None:
         if len(study.directions) == 1:
-            self._determine_single_objective_sampler(study, trial, search_space)
+            self._sampler = self._determine_single_objective_sampler(study, trial, search_space)
         else:
-            self._determine_multi_objective_sampler(study, trial, search_space)
+            self._sampler = self._determine_multi_objective_sampler(study, trial, search_space)
 
     def infer_relative_search_space(
         self, study: Study, trial: FrozenTrial