diff --git a/configs/default_config.ini b/configs/default_config.ini
index 2cad089..f9cc260 100644
--- a/configs/default_config.ini
+++ b/configs/default_config.ini
@@ -32,14 +32,14 @@ min_species_size = 1
 
 [gene-bias]
 bias_init_mean = 0.0
-bias_init_stdev = 1.0
+bias_init_std = 1.0
 bias_mutate_power = 0.5
 bias_mutate_rate = 0.7
 bias_replace_rate = 0.1
 
 [gene-response]
 response_init_mean = 1.0
-response_init_stdev = 0.0
+response_init_std = 0.0
 response_mutate_power = 0.0
 response_mutate_rate = 0.0
 response_replace_rate = 0.0
@@ -56,7 +56,7 @@ aggregation_replace_rate = 0.0
 
 [gene-weight]
 weight_init_mean = 0.0
-weight_init_stdev = 1.0
+weight_init_std = 1.0
 weight_mutate_power = 0.5
 weight_mutate_rate = 0.8
 weight_replace_rate = 0.1
diff --git a/examples/a.py b/examples/a.py
new file mode 100644
index 0000000..5e874ce
--- /dev/null
+++ b/examples/a.py
@@ -0,0 +1,48 @@
+import numpy as np
+
+import jax.numpy as jnp
+import jax
+
+a = jnp.array([1, 0, 1, 0, np.nan])
+b = jnp.array([1, 1, 1, 1, 1])
+c = jnp.array([1, 1, 1, 1, 1])
+
+full = jnp.array([
+    [1, 1, 1],
+    [0, 1, 1],
+    [1, 1, 1],
+    [0, 1, 1],
+])
+
+print(jnp.column_stack([a[:, None], b[:, None], c[:, None]]))
+
+aux0 = full[:, 0, None]
+aux1 = full[:, 1, None]
+
+print(aux0, aux0.shape)
+
+print(jnp.concatenate([aux0, aux1], axis=1))
+
+f_a = jnp.array([False, False, True, True])
+f_b = jnp.array([True, False, False, False])
+
+print(jnp.logical_and(f_a, f_b))
+print(f_a & f_b)
+
+print(f_a + jnp.nan * 0.0)
+print(f_a + 1 * 0.0)
+
+
+@jax.jit
+def main():
+    return func('happy') + func('sad')
+
+
+def func(x):
+    if x == 'happy':
+        return 1
+    else:
+        return 2
+
+
+print(main())
\ No newline at end of file
diff --git a/neat/genome/graph.py b/neat/genome/graph.py
index 09c32df..15592f2 100644
--- a/neat/genome/graph.py
+++ b/neat/genome/graph.py
@@ -9,6 +9,7 @@ from jax import numpy as jnp
 
 # from .configs import fetch_first, I_INT
 from neat.genome.utils import fetch_first, I_INT
+from .utils import unflatten_connections
 
 
 @jit
@@ -129,6 +130,9 @@ def check_cycles(nodes: Array, connections: Array, from_idx: Array, to_idx: Arra
         check_cycles(nodes, connections, 0, 3) -> False
         check_cycles(nodes, connections, 1, 0) -> False
     """
+
+    connections = unflatten_connections(nodes, connections)
+
     connections_enable = ~jnp.isnan(connections[0, :, :])
 
     connections_enable = connections_enable.at[from_idx, to_idx].set(True)
diff --git a/neat/genome/mutate_.py b/neat/genome/mutate_.py
index 384cc67..693fa31 100644
--- a/neat/genome/mutate_.py
+++ b/neat/genome/mutate_.py
@@ -10,7 +10,7 @@ import jax
 from jax import numpy as jnp
 from jax import jit, Array
 
-from .utils import fetch_random, fetch_first, I_INT, unflatten_connections
+from .utils import fetch_random, fetch_first, I_INT
 from .genome_ import add_node, delete_node_by_idx, delete_connection_by_idx, add_connection
 from .graph import check_cycles
 
@@ -25,44 +25,30 @@ def mutate(rand_key: Array, nodes: Array, connections: Array, new_node_key: int,
     :param jit_config:
     :return:
     """
-
-    def m_add_node(rk, n, c):
-        return mutate_add_node(rk, n, c, new_node_key, jit_config['bias_init_mean'], jit_config['response_init_mean'],
-                               jit_config['activation_default'], jit_config['aggregation_default'])
-
-    def m_add_connection(rk, n, c):
-        return mutate_add_connection(rk, n, c, jit_config['input_idx'], jit_config['output_idx'])
-
-    def m_delete_node(rk, n, c):
-        return mutate_delete_node(rk, n, c, jit_config['input_idx'], jit_config['output_idx'])
-
-    def m_delete_connection(rk, n, c):
-        return mutate_delete_connection(rk, n, c)
-
     r1, r2, r3, r4, rand_key = jax.random.split(rand_key, 5)
 
     # structural mutations
     # mutate add node
     r = rand(r1)
-    aux_nodes, aux_connections = m_add_node(r1, nodes, connections)
+    aux_nodes, aux_connections = mutate_add_node(r1, nodes, connections, new_node_key, jit_config)
     nodes = jnp.where(r < jit_config['node_add_prob'], aux_nodes, nodes)
     connections = jnp.where(r < jit_config['node_add_prob'], aux_connections, connections)
 
     # mutate add connection
     r = rand(r2)
-    aux_nodes, aux_connections = m_add_connection(r3, nodes, connections)
+    aux_nodes, aux_connections = mutate_add_connection(r3, nodes, connections, jit_config)
     nodes = jnp.where(r < jit_config['conn_add_prob'], aux_nodes, nodes)
     connections = jnp.where(r < jit_config['conn_add_prob'], aux_connections, connections)
 
     # mutate delete node
     r = rand(r3)
-    aux_nodes, aux_connections = m_delete_node(r2, nodes, connections)
+    aux_nodes, aux_connections = mutate_delete_node(r2, nodes, connections, jit_config)
     nodes = jnp.where(r < jit_config['node_delete_prob'], aux_nodes, nodes)
     connections = jnp.where(r < jit_config['node_delete_prob'], aux_connections, connections)
 
     # mutate delete connection
     r = rand(r4)
-    aux_nodes, aux_connections = m_delete_connection(r4, nodes, connections)
+    aux_nodes, aux_connections = mutate_delete_connection(r4, nodes, connections)
     nodes = jnp.where(r < jit_config['conn_delete_prob'], aux_nodes, nodes)
     connections = jnp.where(r < jit_config['conn_delete_prob'], aux_connections, connections)
 
@@ -72,7 +58,6 @@ def mutate(rand_key: Array, nodes: Array, connections: Array, new_node_key: int,
     return nodes, connections
 
 
-@jit
 def mutate_values(rand_key: Array, nodes: Array, cons: Array, jit_config: Dict) -> Tuple[Array, Array]:
     """
     Mutate values of nodes and connections.
@@ -88,30 +73,41 @@ def mutate_values(rand_key: Array, nodes: Array, cons: Array, jit_config: Dict)
     """
 
     k1, k2, k3, k4, k5, rand_key = jax.random.split(rand_key, num=6)
-    bias_new = mutate_float_values(k1, nodes[:, 1], bias_mean, bias_std,
-                                   bias_mutate_strength, bias_mutate_rate, bias_replace_rate)
-    response_new = mutate_float_values(k2, nodes[:, 2], response_mean, response_std,
-                                       response_mutate_strength, response_mutate_rate, response_replace_rate)
-    weight_new = mutate_float_values(k3, cons[:, 2], weight_mean, weight_std,
-                                     weight_mutate_strength, weight_mutate_rate, weight_replace_rate)
-    act_new = mutate_int_values(k4, nodes[:, 3], act_list, act_replace_rate)
-    agg_new = mutate_int_values(k5, nodes[:, 4], agg_list, agg_replace_rate)
 
-    # mutate enabled
+    # bias
+    bias_new = mutate_float_values(k1, nodes[:, 1], jit_config['bias_init_mean'], jit_config['bias_init_std'],
+                                   jit_config['bias_mutate_power'], jit_config['bias_mutate_rate'],
+                                   jit_config['bias_replace_rate'])
+
+    # response
+    response_new = mutate_float_values(k2, nodes[:, 2], jit_config['response_init_mean'],
+                                       jit_config['response_init_std'], jit_config['response_mutate_power'],
+                                       jit_config['response_mutate_rate'], jit_config['response_replace_rate'])
+
+    # weight
+    weight_new = mutate_float_values(k3, cons[:, 2], jit_config['weight_init_mean'], jit_config['weight_init_std'],
+                                     jit_config['weight_mutate_power'], jit_config['weight_mutate_rate'],
+                                     jit_config['weight_replace_rate'])
+
+    # activation
+    act_new = mutate_int_values(k4, nodes[:, 3], jit_config['activation_options'],
+                                jit_config['activation_replace_rate'])
+
+    # aggregation
+    agg_new = mutate_int_values(k5, nodes[:, 4], jit_config['aggregation_options'],
+                                jit_config['aggregation_replace_rate'])
+
+    # enabled
     r = jax.random.uniform(rand_key, cons[:, 3].shape)
-    enabled_new = jnp.where(r < enabled_reverse_rate, 1 - cons[:, 3], cons[:, 3])
-    enabled_new = jnp.where(~jnp.isnan(cons[:, 3]), enabled_new, jnp.nan)
+    enabled_new = jnp.where(r < jit_config['enable_mutate_rate'], 1 - cons[:, 3], cons[:, 3])
+
+    # merge
+    nodes = jnp.column_stack([nodes[:, 0], bias_new, response_new, act_new, agg_new])
+    cons = jnp.column_stack([cons[:, 0], cons[:, 1], weight_new, enabled_new])
 
-    nodes = nodes.at[:, 1].set(bias_new)
-    nodes = nodes.at[:, 2].set(response_new)
-    nodes = nodes.at[:, 3].set(act_new)
-    nodes = nodes.at[:, 4].set(agg_new)
-    cons = cons.at[:, 2].set(weight_new)
-    cons = cons.at[:, 3].set(enabled_new)
     return nodes, cons
 
 
-@jit
 def mutate_float_values(rand_key: Array, old_vals: Array, mean: float, std: float,
                         mutate_strength: float, mutate_rate: float, replace_rate: float) -> Array:
     """
@@ -132,19 +128,26 @@ def mutate_float_values(rand_key: Array, old_vals: Array, mean: float, std: floa
     k1, k2, k3, rand_key = jax.random.split(rand_key, num=4)
     noise = jax.random.normal(k1, old_vals.shape) * mutate_strength
     replace = jax.random.normal(k2, old_vals.shape) * std + mean
+
     r = jax.random.uniform(k3, old_vals.shape)
+
+    # default
     new_vals = old_vals
+
+    # r in [0, mutate_rate), mutate
     new_vals = jnp.where(r < mutate_rate, new_vals + noise, new_vals)
+
+    # r in [mutate_rate, mutate_rate + replace_rate), replace
     new_vals = jnp.where(
-        jnp.logical_and(mutate_rate < r, r < mutate_rate + replace_rate),
-        replace,
+        (mutate_rate < r) & (r < mutate_rate + replace_rate),
+        replace + new_vals * 0.0,  # in case of nan replace to values
         new_vals
     )
+
     new_vals = jnp.where(~jnp.isnan(old_vals), new_vals, jnp.nan)
     return new_vals
 
 
-@jit
 def mutate_int_values(rand_key: Array, old_vals: Array, val_list: Array, replace_rate: float) -> Array:
     """
     Mutate integer values (act, agg) of a given array.
@@ -161,26 +164,20 @@ def mutate_int_values(rand_key: Array, old_vals: Array, val_list: Array, replace
     k1, k2, rand_key = jax.random.split(rand_key, num=3)
     replace_val = jax.random.choice(k1, val_list, old_vals.shape)
     r = jax.random.uniform(k2, old_vals.shape)
-    new_vals = old_vals
-    new_vals = jnp.where(r < replace_rate, replace_val, new_vals)
-    new_vals = jnp.where(~jnp.isnan(old_vals), new_vals, jnp.nan)
+    new_vals = jnp.where(r < replace_rate, replace_val + old_vals * 0.0, old_vals)  # in case of nan replace to values
+
     return new_vals
 
 
-@jit
 def mutate_add_node(rand_key: Array, nodes: Array, cons: Array, new_node_key: int,
-                    default_bias: float = 0, default_response: float = 1,
-                    default_act: int = 0, default_agg: int = 0) -> Tuple[Array, Array]:
+                    jit_config: Dict) -> Tuple[Array, Array]:
     """
     Randomly add a new node from splitting a connection.
     :param rand_key:
     :param new_node_key:
     :param nodes:
     :param cons:
-    :param default_bias:
-    :param default_response:
-    :param default_act:
-    :param default_agg:
+    :param jit_config:
     :return:
     """
     # randomly choose a connection
@@ -192,12 +189,13 @@ def mutate_add_node(rand_key: Array, nodes: Array, cons: Array, new_node_key: in
     def successful_add_node():
         # disable the connection
         new_nodes, new_cons = nodes, cons
+
+        # set enable to false
         new_cons = new_cons.at[idx, 3].set(False)
 
         # add a new node
-        new_nodes, new_cons = \
-            add_node(new_nodes, new_cons, new_node_key,
-                     bias=default_bias, response=default_response, act=default_act, agg=default_agg)
+        new_nodes, new_cons = add_node(new_nodes, new_cons, new_node_key, bias=0, response=1,
+                                       act=jit_config['activation_default'], agg=jit_config['aggregation_default'])
 
         # add two new connections
         w = new_cons[idx, 2]
@@ -211,59 +209,55 @@ def mutate_add_node(rand_key: Array, nodes: Array, cons: Array, new_node_key: in
     return nodes, cons
 
 
-# TODO: Need we really need to delete a node?
+# TODO: Do we really need to delete a node?
 @jit
-def mutate_delete_node(rand_key: Array, nodes: Array, cons: Array,
-                       input_keys: Array, output_keys: Array) -> Tuple[Array, Array]:
+def mutate_delete_node(rand_key: Array, nodes: Array, cons: Array, jit_config: Dict) -> Tuple[Array, Array]:
     """
     Randomly delete a node. Input and output nodes are not allowed to be deleted.
     :param rand_key:
     :param nodes:
     :param cons:
-    :param input_keys:
-    :param output_keys:
+    :param jit_config:
     :return:
     """
     # randomly choose a node
-    node_key, node_idx = choice_node_key(rand_key, nodes, input_keys, output_keys,
-                                         allow_input_keys=False, allow_output_keys=False)
+    key, idx = choice_node_key(rand_key, nodes, jit_config['input_idx'], jit_config['output_idx'],
+                               allow_input_keys=False, allow_output_keys=False)
 
     def nothing():
         return nodes, cons
 
     def successful_delete_node():
         # delete the node
-        aux_nodes, aux_cons = delete_node_by_idx(nodes, cons, node_idx)
+        aux_nodes, aux_cons = delete_node_by_idx(nodes, cons, idx)
 
         # delete all connections
-        aux_cons = jnp.where(((aux_cons[:, 0] == node_key) | (aux_cons[:, 1] == node_key))[:, jnp.newaxis],
+        aux_cons = jnp.where(((aux_cons[:, 0] == key) | (aux_cons[:, 1] == key))[:, None],
                              jnp.nan, aux_cons)
 
         return aux_nodes, aux_cons
 
-    nodes, cons = jax.lax.cond(node_idx == I_INT, nothing, successful_delete_node)
+    nodes, cons = jax.lax.cond(idx == I_INT, nothing, successful_delete_node)
 
     return nodes, cons
 
 
 @jit
-def mutate_add_connection(rand_key: Array, nodes: Array, cons: Array,
-                          input_keys: Array, output_keys: Array) -> Tuple[Array, Array]:
+def mutate_add_connection(rand_key: Array, nodes: Array, cons: Array, jit_config: Dict) -> Tuple[Array, Array]:
     """
     Randomly add a new connection. The output node is not allowed to be an input node. If in feedforward networks,
     cycles are not allowed.
     :param rand_key:
     :param nodes:
     :param cons:
-    :param input_keys:
-    :param output_keys:
+    :param jit_config:
     :return:
     """
     # randomly choose two nodes
     k1, k2 = jax.random.split(rand_key, num=2)
-    i_key, from_idx = choice_node_key(k1, nodes, input_keys, output_keys,
+    i_key, from_idx = choice_node_key(k1, nodes, jit_config['input_idx'], jit_config['output_idx'],
                                       allow_input_keys=True, allow_output_keys=True)
-    o_key, to_idx = choice_node_key(k2, nodes, input_keys, output_keys,
+    o_key, to_idx = choice_node_key(k2, nodes, jit_config['input_idx'], jit_config['output_idx'],
                                     allow_input_keys=False, allow_output_keys=True)
 
     con_idx = fetch_first((cons[:, 0] == i_key) & (cons[:, 1] == o_key))
@@ -280,8 +274,8 @@ def mutate_add_connection(rand_key: Array, nodes: Array, cons: Array,
         return nodes, cons
 
     is_already_exist = con_idx != I_INT
-    unflattened = unflatten_connections(nodes, cons)
-    is_cycle = check_cycles(nodes, unflattened, from_idx, to_idx)
+
+    is_cycle = check_cycles(nodes, cons, from_idx, to_idx)
 
     choice = jnp.where(is_already_exist, 0, jnp.where(is_cycle, 1, 2))
     nodes, cons = jax.lax.switch(choice, [already_exist, cycle, successful])
@@ -311,7 +305,6 @@ def mutate_delete_connection(rand_key: Array, nodes: Array, cons: Array):
     return nodes, cons
 
 
-@partial(jit, static_argnames=('allow_input_keys', 'allow_output_keys'))
 def choice_node_key(rand_key: Array, nodes: Array,
                     input_keys: Array, output_keys: Array,
                     allow_input_keys: bool = False, allow_output_keys: bool = False) -> Tuple[Array, Array]:
diff --git a/neat/genome/utils_.py b/neat/genome/utils_.py
new file mode 100644
index 0000000..27ab91a
--- /dev/null
+++ b/neat/genome/utils_.py
@@ -0,0 +1,102 @@
+from functools import partial
+
+import jax
+from jax import numpy as jnp, Array
+from jax import jit, vmap
+
+I_INT = jnp.iinfo(jnp.int32).max  # infinite int
+EMPTY_NODE = jnp.full((1, 5), jnp.nan)
+EMPTY_CON = jnp.full((1, 4), jnp.nan)
+
+
+@jit
+def unflatten_connections(nodes: Array, cons: Array):
+    """
+    transform the (C, 4) connections to (2, N, N)
+    :param nodes: (N, 5)
+    :param cons: (C, 4)
+    :return:
+    """
+    N = nodes.shape[0]
+    node_keys = nodes[:, 0]
+    i_keys, o_keys = cons[:, 0], cons[:, 1]
+    i_idxs = vmap(fetch_first, in_axes=(0, None))(i_keys, node_keys)
+    i_idxs = key_to_indices(i_keys, node_keys)
+    o_idxs = key_to_indices(o_keys, node_keys)
+    res = jnp.full((2, N, N), jnp.nan)
+
+    # Is interesting that jax use clip when attach data in array
+    # however, it will do nothing set values in an array
+    res = res.at[0, i_idxs, o_idxs].set(cons[:, 2])
+    res = res.at[1, i_idxs, o_idxs].set(cons[:, 3])
+
+    return res
+
+
+@partial(vmap, in_axes=(0, None))
+def key_to_indices(key, keys):
+    return fetch_first(key == keys)
+
+
+@jit
+def fetch_first(mask, default=I_INT) -> Array:
+    """
+    fetch the first True index
+    :param mask: array of bool
+    :param default: the default value if no element satisfying the condition
+    :return: the index of the first element satisfying the condition. if no element satisfying the condition, return I_INT
+    example:
+    >>> a = jnp.array([1, 2, 3, 4, 5])
+    >>> fetch_first(a > 3)
+    3
+    >>> fetch_first(a > 30)
+    I_INT
+    """
+    idx = jnp.argmax(mask)
+    return jnp.where(mask[idx], idx, default)
+
+
+@jit
+def fetch_last(mask, default=I_INT) -> Array:
+    """
+    similar to fetch_first, but fetch the last True index
+    """
+    reversed_idx = fetch_first(mask[::-1], default)
+    return jnp.where(reversed_idx == -1, -1, mask.shape[0] - reversed_idx - 1)
+
+
+@jit
+def fetch_random(rand_key, mask, default=I_INT) -> Array:
+    """
+    similar to fetch_first, but fetch a random True index
+    """
+    true_cnt = jnp.sum(mask)
+    cumsum = jnp.cumsum(mask)
+    target = jax.random.randint(rand_key, shape=(), minval=1, maxval=true_cnt + 1)
+    mask = jnp.where(true_cnt == 0, False, cumsum >= target)
+    return fetch_first(mask, default)
+
+
+@jit
+def argmin_with_mask(arr: Array, mask: Array) -> Array:
+    masked_arr = jnp.where(mask, arr, jnp.inf)
+    min_idx = jnp.argmin(masked_arr)
+    return min_idx
+
+
+if __name__ == '__main__':
+
+    a = jnp.array([1, 2, 3, 4, 5])
+    print(fetch_first(a > 3))
+    print(fetch_first(a > 30))
+
+    print(fetch_last(a > 3))
+    print(fetch_last(a > 30))
+
+    rand_key = jax.random.PRNGKey(0)
+
+    for t in [-1, 0, 1, 2, 3, 4, 5]:
+        for _ in range(10):
+            rand_key, _ = jax.random.split(rand_key)
+            print(jax.random.randint(rand_key, shape=(), minval=1, maxval=2))
+            print(t, fetch_random(rand_key, a > t))