diff --git a/tensorneat/algorithm/neat/ga/mutation/default.py b/tensorneat/algorithm/neat/ga/mutation/default.py
index 81252bd..54f492f 100644
--- a/tensorneat/algorithm/neat/ga/mutation/default.py
+++ b/tensorneat/algorithm/neat/ga/mutation/default.py
@@ -37,6 +37,10 @@ class DefaultMutation(BaseMutation):
         return nodes, conns
 
     def mutate_structure(self, state, randkey, genome, nodes, conns, new_node_key):
+
+        remain_node_space = jnp.isnan(nodes[:, 0]).sum()
+        remain_conn_space = jnp.isnan(conns[:, 0]).sum()
+
         def mutate_add_node(key_, nodes_, conns_):
             i_key, o_key, idx = self.choice_connection_key(key_, conns_)
 
@@ -68,7 +72,7 @@ class DefaultMutation(BaseMutation):
                 return new_nodes, new_conns
 
             return jax.lax.cond(
-                idx == I_INF,
+                (idx == I_INF) & (remain_node_space < 1) & (remain_conn_space < 2),
                 lambda: (nodes_, conns_),  # do nothing
                 successful_add_node,
             )
@@ -150,7 +154,9 @@ class DefaultMutation(BaseMutation):
                 return jax.lax.cond(
                     is_already_exist,
                     already_exist,
-                    lambda: jax.lax.cond(is_cycle, nothing, successful),
+                    lambda: jax.lax.cond(
+                        is_cycle & (remain_conn_space < 1), nothing, successful
+                    ),
                 )
 
             elif genome.network_type == "recurrent":
diff --git a/tensorneat/examples/func_fit/xor.py b/tensorneat/examples/func_fit/xor.py
index aaf83a2..652c68c 100644
--- a/tensorneat/examples/func_fit/xor.py
+++ b/tensorneat/examples/func_fit/xor.py
@@ -11,8 +11,8 @@ if __name__ == "__main__":
                 genome=DefaultGenome(
                     num_inputs=3,
                     num_outputs=1,
-                    max_nodes=100,
-                    max_conns=200,
+                    max_nodes=5,
+                    max_conns=10,
                     node_gene=DefaultNodeGene(
                         activation_default=Act.tanh,
                         activation_options=(Act.tanh,),
diff --git a/tensorneat/pipeline.py b/tensorneat/pipeline.py
index 6e37744..caa5afa 100644
--- a/tensorneat/pipeline.py
+++ b/tensorneat/pipeline.py
@@ -173,8 +173,27 @@ class Pipeline:
         member_count = jax.device_get(self.algorithm.member_count(state))
         species_sizes = [int(i) for i in member_count if i > 0]
 
+        pop = jax.device_get(pop)
+        pop_nodes, pop_conns = pop  # (P, N, NL), (P, C, CL)
+        nodes_cnt = (~np.isnan(pop_nodes[:, :, 0])).sum(axis=1)  # (P,)
+        conns_cnt = (~np.isnan(pop_conns[:, :, 0])).sum(axis=1)  # (P,)
+
+        max_node_cnt, min_node_cnt, mean_node_cnt = (
+            max(nodes_cnt),
+            min(nodes_cnt),
+            np.mean(nodes_cnt),
+        )
+
+        max_conn_cnt, min_conn_cnt, mean_conn_cnt = (
+            max(conns_cnt),
+            min(conns_cnt),
+            np.mean(conns_cnt),
+        )
+
         print(
             f"Generation: {self.algorithm.generation(state)}, Cost time: {cost_time * 1000:.2f}ms\n",
+            f"\tnode counts: max: {max_node_cnt}, min: {min_node_cnt}, mean: {mean_node_cnt:.2f}\n",
+            f"\tconn counts: max: {max_conn_cnt}, min: {min_conn_cnt}, mean: {mean_conn_cnt:.2f}\n",
             f"\tspecies: {len(species_sizes)}, {species_sizes}\n",
             f"\tfitness: valid cnt: {len(valid_fitnesses)}, max: {max_f:.4f}, min: {min_f:.4f}, mean: {mean_f:.4f}, std: {std_f:.4f}\n",
         )