modify method cal_spawn_numbers

spawn_number = previous_size + (target_spawn_number - previous_size) * jit_config['spawn_number_move_rate']

algorithms/neat/__init__.py

@@ -2,7 +2,7 @@
 contains operations on a single genome. e.g. forward, mutate, crossover, etc.
 """
 from .genome import create_forward_function, topological_sort, unflatten_connections, initialize_genomes
-from .population import update_species, create_next_generation, speciate
+from .population import update_species, create_next_generation, speciate, tell
 
 from .genome.activations import act_name2func
 from .genome.aggregations import agg_name2func

algorithms/neat/genome/forward.py

@@ -100,4 +100,4 @@ def create_forward_function(config):
     elif config['forward_way'] == 'common':
         return jit(common_forward)
 
-    return forward
+    return jit(forward)

algorithms/neat/population.py

@@ -11,6 +11,28 @@ from jax import jit, vmap, Array, numpy as jnp
 from .genome import distance, mutate, crossover, I_INT, fetch_first, rank_elements
 
 
+@jit
+def tell(fitness, randkey, pop_nodes, pop_cons, species_info, idx2species, center_nodes, center_cons, generation,
+         jit_config):
+
+    generation += 1
+
+    k1, k2, randkey = jax.random.split(randkey, 3)
+
+    species_info, center_nodes, center_cons, winner, loser, elite_mask = \
+        update_species(k1, fitness, species_info, idx2species, center_nodes,
+                       center_cons, generation, jit_config)
+
+    pop_nodes, pop_cons = create_next_generation(k2, pop_nodes, pop_cons, winner, loser,
+                                                 elite_mask, generation, jit_config)
+
+    idx2species, center_nodes, center_cons, species_info = speciate(
+        pop_nodes, pop_cons, species_info, center_nodes, center_cons, generation,
+        jit_config)
+
+    return randkey, pop_nodes, pop_cons, species_info, idx2species, center_nodes, center_cons, generation
+
+
 @jit
 def update_species(randkey, fitness, species_info, idx2species, center_nodes, center_cons, generation, jit_config):
     """
@@ -110,7 +132,13 @@ def cal_spawn_numbers(species_info, jit_config):
     spawn_number_rate = rank_score / denominator  # obtain [0.5, 0.33, 0.17]
     spawn_number_rate = jnp.where(is_species_valid, spawn_number_rate, 0)  # set invalid species to 0
 
-    spawn_number = jnp.floor(spawn_number_rate * jit_config['pop_size']).astype(jnp.int32)  # calculate member
+    target_spawn_number = jnp.floor(spawn_number_rate * jit_config['pop_size'])  # calculate member
+
+    # Avoid too much variation of numbers in a species
+    previous_size = species_info[:, 3].astype(jnp.int32)
+    spawn_number = previous_size + (target_spawn_number - previous_size) * jit_config['spawn_number_move_rate']
+
+    spawn_number = spawn_number.astype(jnp.int32)
 
     # must control the sum of spawn_number to be equal to pop_size
     error = jit_config['pop_size'] - jnp.sum(spawn_number)

configs/configer.py

@@ -44,7 +44,8 @@ jit_config_keys = [
     "pop_size",
     "genome_elitism",
     "survival_threshold",
-    "species_elitism"
+    "species_elitism",
+    "spawn_number_move_rate"
 ]
 
 

configs/default_config.ini

@@ -2,7 +2,7 @@
 num_inputs = 2
 num_outputs = 1
 init_maximum_nodes = 50
-init_maximum_connections = 200
+init_maximum_connections = 50
 init_maximum_species = 10
 expand_coe = 1.5
 pre_expand_threshold = 0.75
@@ -13,7 +13,7 @@ batch_size = 4
 fitness_threshold = 100000
 generation_limit = 1000
 fitness_criterion = "max"
-pop_size = 150
+pop_size = 2000
 
 [genome]
 compatibility_disjoint = 1.0
@@ -31,6 +31,7 @@ max_stagnation = 15
 genome_elitism = 2
 survival_threshold = 0.2
 min_species_size = 1
+spawn_number_move_rate = 0.5
 
 [gene-bias]
 bias_init_mean = 0.0

examples/xor.py

@@ -1,3 +1,4 @@
+import jax
 import numpy as np
 
 from configs import Configer
@@ -14,8 +15,9 @@ def evaluate(forward_func):
     :return:
     """
     outs = forward_func(xor_inputs)
+    outs = jax.device_get(outs)
     fitnesses = 4 - np.sum((outs - xor_outputs) ** 2, axis=(1, 2))
-    return np.array(fitnesses)  # returns a list
+    return fitnesses
 
 
 def main():

pipeline.py

@@ -48,6 +48,26 @@ class Pipeline:
         self.pop_topological_sort = jit(vmap(neat.topological_sort))
         self.forward = neat.create_forward_function(config)
 
+        # fitness_lower = np.zeros(self.P, dtype=np.float32)
+        # randkey_lower = np.zeros(2, dtype=np.uint32)
+        # pop_nodes_lower = np.zeros((self.P, self.N, 5), dtype=np.float32)
+        # pop_cons_lower = np.zeros((self.P, self.C, 4), dtype=np.float32)
+        # species_info_lower = np.zeros((self.S, 4), dtype=np.float32)
+        # idx2species_lower = np.zeros(self.P, dtype=np.float32)
+        # center_nodes_lower = np.zeros((self.S, self.N, 5), dtype=np.float32)
+        # center_cons_lower = np.zeros((self.S, self.C, 4), dtype=np.float32)
+        #
+        # self.tell_func = jit(neat.tell).lower(fitness_lower,
+        #                                       randkey_lower,
+        #                                       pop_nodes_lower,
+        #                                       pop_cons_lower,
+        #                                       species_info_lower,
+        #                                       idx2species_lower,
+        #                                       center_nodes_lower,
+        #                                       center_cons_lower,
+        #                                       0,
+        #                                       self.jit_config).compile()
+
     def ask(self):
         """
         Creates a function that receives a genome and returns a forward function.
@@ -75,21 +95,12 @@ class Pipeline:
         assert self.config['forward_way'] == 'common'
         return lambda x: self.forward(x, pop_seqs, self.pop_nodes, u_pop_cons)
 
-    def tell(self, fitnesses):
-        self.generation += 1
+    def tell(self, fitness):
 
-        k1, k2, self.randkey = jax.random.split(self.randkey, 3)
-
-        self.species_info, self.center_nodes, self.center_cons, winner, loser, elite_mask = \
-            neat.update_species(k1, fitnesses, self.species_info, self.idx2species, self.center_nodes,
-                                self.center_cons, self.generation, self.jit_config)
-
-        self.pop_nodes, self.pop_cons = neat.create_next_generation(k2, self.pop_nodes, self.pop_cons, winner, loser,
-                                                                    elite_mask, self.generation, self.jit_config)
-
-        self.idx2species, self.center_nodes, self.center_cons, self.species_info = neat.speciate(
-            self.pop_nodes, self.pop_cons, self.species_info, self.center_nodes, self.center_cons, self.generation,
-            self.jit_config)
+        self.randkey, self.pop_nodes, self.pop_cons, self.species_info, self.idx2species, self.center_nodes, \
+            self.center_cons, self.generation = neat.tell(fitness, self.randkey, self.pop_nodes, self.pop_cons,
+                                                          self.species_info, self.idx2species, self.center_nodes,
+                                                          self.center_cons, self.generation, self.jit_config)
 
     def auto_run(self, fitness_func, analysis: Union[Callable, str] = "default"):
         for _ in range(self.config['generation_limit']):