diff --git a/package/samplers/catcma/catcma.py b/package/samplers/catcma/catcma.py
index dd335ef8..371ee9ea 100644
--- a/package/samplers/catcma/catcma.py
+++ b/package/samplers/catcma/catcma.py
@@ -199,23 +199,36 @@ def sample_relative(
         solution_trials = self._get_solution_trials(completed_trials, optimizer.generation)
 
         if len(solution_trials) >= popsize:
-            solutions: List[Tuple[np.ndarray, float]] = []
+            # Calculate the number of categorical variables and maximum number of choices
+            num_categorical_vars = len(categorical_search_space)
+            max_num_choices = max(
+                len(space.choices) for space in categorical_search_space.values()
+            )
+
+            # Prepare solutions list
+            solutions: List[Tuple[Tuple[np.ndarray, np.ndarray], float]] = []
+
             for t in solution_trials[:popsize]:
                 assert t.value is not None, "completed trials must have a value"
                 # Convert Optuna's representation to cmaes.CatCma's internal representation.
+
+                # Convert numerical parameters
                 x = trans.transform({k: t.params[k] for k in numerical_search_space.keys()})
+
                 # Convert categorial values to one-hot vectors.
                 # Example:
                 #   choices = ['a', 'b', 'c']
                 #   value = 'b'
                 #   one_hot_vec = [False, True, False]
-                c = np.asarray(
-                    [
-                        [c == v for c in categorical_search_space[k].choices]
-                        for k, v in t.params.items()
-                        if k in categorical_search_space.keys()
-                    ]
-                )
+                c = np.zeros((num_categorical_vars, max_num_choices))
+
+                for idx, k in enumerate(categorical_search_space.keys()):
+                    choices = categorical_search_space[k].choices
+                    v = t.params.get(k)
+                    if v is not None:
+                        index = choices.index(v)
+                        c[idx, index] = 1
+
                 y = t.value if study.direction == StudyDirection.MINIMIZE else -t.value
                 solutions.append(((x, c), y))  # type: ignore