diff --git a/algorithms/neat/pipeline.py b/algorithms/neat/pipeline.py
index c9d77df..4c1085d 100644
--- a/algorithms/neat/pipeline.py
+++ b/algorithms/neat/pipeline.py
@@ -2,7 +2,6 @@ from typing import List, Union, Tuple, Callable
 import time
 
 import jax
-import jax.numpy as jnp
 import numpy as np
 
 from .species import SpeciesController
@@ -19,6 +18,7 @@ class Pipeline:
     def __init__(self, config, seed=42):
         self.generation_timestamp = time.time()
         self.randkey = jax.random.PRNGKey(seed)
+        np.random.seed(seed)
 
         self.config = config
         self.N = config.basic.init_maximum_nodes
@@ -34,9 +34,6 @@ class Pipeline:
         self.generation = 0
         self.species_controller.init_speciate(self.pop_nodes, self.pop_connections)
 
-        # self.species_controller.speciate(self.pop_nodes, self.pop_connections,
-        #                                  self.generation, self.o2o_distance, self.o2m_distance)
-
         self.best_fitness = float('-inf')
 
     def ask(self, batch: bool):
diff --git a/algorithms/neat/species.py b/algorithms/neat/species.py
index feaf47f..f8b947b 100644
--- a/algorithms/neat/species.py
+++ b/algorithms/neat/species.py
@@ -239,7 +239,9 @@ class SpeciesController:
         self.species = {}
         # int -> idx in the pop_nodes, pop_connections of elitism
         # (int, int) -> the father and mother idx to be crossover
+
         crossover_pair: List[Union[int, Tuple[int, int]]] = []
+
         for spawn, s in zip(spawn_amounts, remaining_species):
             assert spawn >= self.genome_elitism
 
@@ -264,10 +266,8 @@ class SpeciesController:
             # only use good genomes to crossover
             sorted_members = sorted_members[:repro_cutoff]
 
-            # Randomly choose parents and produce the number of offspring allotted to the species.
-            for _ in range(spawn):
-                # allow to replace, for the case that the species only has one genome
-                c1, c2 = np.random.choice(len(sorted_members), size=2, replace=True)
+            list_idx1, list_idx2 = np.random.choice(len(sorted_members), size=(2, spawn), replace=True)
+            for c1, c2 in zip(list_idx1, list_idx2):
                 idx1, fitness1 = sorted_members[c1], sorted_fitnesses[c1]
                 idx2, fitness2 = sorted_members[c2], sorted_fitnesses[c2]
                 if fitness1 >= fitness2: