Added softplus activation.

Rescaled some activation functions to have most of their "interesting" behavior confined to [-1, 1]x[-1, 1].
Renamed genome compatibility parameters for clarity.
Fixed bug in StatisticsReporter.get_average_fitness.
Added TODO comments.
Updated XOR config file to look more like re-saved version.

Author: CodeReclaimers

examples/xor/xor2_config

@@ -3,65 +3,76 @@
 # The `NEAT` section specifies parameters particular to the NEAT algorithm
 # or the experiment itself.  This is the only required section.
 [NEAT]
-pop_size              = 150
-max_fitness_threshold = 0.95
+max_fitness_threshold = 0.99
+pop_size              = 1000
 reset_on_extinction   = False
 
 [DefaultGenome]
-num_inputs              = 2
-num_hidden              = 0
-num_outputs             = 1
-initial_connection      = full
-feed_forward            = 0
-# genome compatibility options
-compatibility_threshold = 3.0
-disjoint_coefficient    = 1.0
-weight_coefficient      = 0.5
-# connection add/remove rates
-conn_add_prob           = 0.5
-conn_delete_prob        = 0.25
-# node add/remove rates
-node_add_prob           = 0.1
-node_delete_prob        = 0.05
 # node activation options
 activation_default      = sigmoid
-activation_options      = sigmoid
 activation_mutate_rate  = 0.0
+activation_options      = sigmoid
+
 # node aggregation options
 aggregation_default     = sum
-aggregation_options     = sum
 aggregation_mutate_rate = 0.0
+aggregation_options     = sum
+
 # node bias options
 bias_init_mean          = 0.0
 bias_init_stdev         = 1.0
-bias_replace_rate       = 0.1
-bias_mutate_rate        = 0.7
-bias_mutate_power       = 0.5
 bias_max_value          = 30.0
 bias_min_value          = -30.0
+bias_mutate_power       = 0.5
+bias_mutate_rate        = 0.7
+bias_replace_rate       = 0.1
+
+# genome compatibility options
+compatibility_disjoint_coefficient    = 1.0
+compatibility_threshold = 3.0
+compatibility_weight_coefficient      = 0.5
+
+# connection add/remove rates
+conn_add_prob           = 0.5
+conn_delete_prob        = 0.5
+
+# connection enable options
+enabled_default         = True
+enabled_mutate_rate     = 0.01
+
+feed_forward            = False
+initial_connection      = full
+
+# node add/remove rates
+node_add_prob           = 0.2
+node_delete_prob        = 0.2
+
+# network parameters
+num_hidden              = 0
+num_inputs              = 2
+num_outputs             = 1
+
 # node response options
 response_init_mean      = 5.0
 response_init_stdev     = 0.1
-response_replace_rate   = 0.1
-response_mutate_rate    = 0.2
-response_mutate_power   = 0.1
 response_max_value      = 30.0
 response_min_value      = -30.0
+response_mutate_power   = 0.1
+response_mutate_rate    = 0.2
+response_replace_rate   = 0.1
+
 # connection weight options
-weight_max_value        = 30
-weight_min_value        = -30
 weight_init_mean        = 0.0
 weight_init_stdev       = 1.0
+weight_max_value        = 30
+weight_min_value        = -30
+weight_mutate_power     = 0.5
 weight_mutate_rate      = 0.8
 weight_replace_rate     = 0.1
-weight_mutate_power     = 0.5
-# connection enable options
-enabled_default         = True
-enabled_mutate_rate     = 0.01
 
 [DefaultStagnation]
-species_fitness = median
-max_stagnation  = 10
+species_fitness_func = max
+max_stagnation       = 20
 
 [DefaultReproduction]
 elitism            = 2

neat/activations.py

@@ -3,29 +3,34 @@
 
 
 def sigmoid_activation(z):
-    z = max(-60.0, min(60.0, z))
+    z = max(-60.0, min(60.0, 5.0 * z))
     return 1.0 / (1.0 + math.exp(-z))
 
 
 def tanh_activation(z):
-    z = max(-60.0, min(60.0, z))
+    z = max(-60.0, min(60.0, 2.5 * z))
     return math.tanh(z)
 
 
 def sin_activation(z):
-    z = max(-60.0, min(60.0, z))
+    z = max(-60.0, min(60.0, 5.0 * z))
     return math.sin(z)
 
 
 def gauss_activation(z):
-    z = max(-60.0, min(60.0, z))
-    return math.exp(-0.5 * z**2) / math.sqrt(2 * math.pi)
+    z = max(-3.4, min(3.4, z))
+    return math.exp(-5.0 * z**2)
 
 
 def relu_activation(z):
     return z if z > 0.0 else 0.0
 
 
+def softplus_activation(z):
+    z = max(-60.0, min(60.0, 5.0 * z))
+    return 0.2 * math.log(1 + math.exp(z))
+
+
 def identity_activation(z):
     return z
 
@@ -88,6 +93,7 @@ def __init__(self):
         self.add('sin', sin_activation)
         self.add('gauss', gauss_activation)
         self.add('relu', relu_activation)
+        self.add('softplus', softplus_activation)
         self.add('identity', identity_activation)
         self.add('clamped', clamped_activation)
         self.add('inv', inv_activation)

neat/genes.py

@@ -92,7 +92,7 @@ def distance(self, other, config):
         d = abs(self.bias - other.bias) + abs(self.response - other.response)
         if self.activation != other.activation:
             d += 1.0
-        return d * config.weight_coefficient
+        return d * config.compatibility_weight_coefficient
 
 
 # TODO: Do an ablation study to determine whether the enabled setting is
@@ -110,5 +110,5 @@ def distance(self, other, config):
         d = abs(self.weight - other.weight)
         if self.enabled != other.enabled:
             d += 1.0
-        return d * config.weight_coefficient
+        return d * config.compatibility_weight_coefficient
 

neat/genome.py

@@ -20,8 +20,8 @@ class DefaultGenomeConfig(object):
                 ConfigParameter('num_hidden', int),
                 ConfigParameter('feed_forward', bool),
                 ConfigParameter('compatibility_threshold', float),
-                ConfigParameter('disjoint_coefficient', float),
-                ConfigParameter('weight_coefficient', float),
+                ConfigParameter('compatibility_disjoint_coefficient', float),
+                ConfigParameter('compatibility_weight_coefficient', float),
                 ConfigParameter('conn_add_prob', float),
                 ConfigParameter('conn_delete_prob', float),
                 ConfigParameter('node_add_prob', float),
@@ -298,7 +298,7 @@ def distance(self, other, config):
                     node_distance += n1.distance(n2, config)
 
             max_nodes = max(len(self.nodes), len(other.nodes))
-            node_distance = (node_distance + config.disjoint_coefficient * disjoint_nodes) / max_nodes
+            node_distance = (node_distance + config.compatibility_disjoint_coefficient * disjoint_nodes) / max_nodes
 
         # Compute connection gene differences.
         connection_distance = 0.0
@@ -317,7 +317,7 @@ def distance(self, other, config):
                     connection_distance += c1.distance(c2, config)
 
             max_conn = max(len(self.connections), len(other.connections))
-            connection_distance = (connection_distance + config.disjoint_coefficient * disjoint_connections) / max_conn
+            connection_distance = (connection_distance + config.compatibility_disjoint_coefficient * disjoint_connections) / max_conn
 
         distance = node_distance + connection_distance
         compatible = distance < config.compatibility_threshold

neat/stagnation.py

@@ -4,6 +4,7 @@
 from neat.six_util import iteritems
 
 # TODO: Add a method for the user to change the "is stagnant" computation.
+# TODO: Add a mechanism to prevent stagnation of the top N species.
 
 
 class DefaultStagnation(object):

neat/statistics.py

@@ -31,8 +31,8 @@ def get_average_fitness(self):
         avg_fitness = []
         for stats in self.generation_statistics:
             scores = []
-            for fitness in stats.values():
-                scores.extend(fitness)
+            for species_stats in stats.values():
+                scores.extend(species_stats.values())
             avg_fitness.append(mean(scores))
 
         return avg_fitness