Added ReadTheDocs configuration.

Add custom aggregation function to memory-fixed example.
Modify memory-fixed example configuration to make better progress.

.readthedocs.yaml

@@ -0,0 +1,29 @@
+# .readthedocs.yaml
+# Read the Docs configuration file
+# See https://docs.readthedocs.io/en/stable/config-file/v2.html for details
+
+# Required
+version: 2
+
+# Set the version of Python and other tools you might need
+build:
+  os: ubuntu-20.04
+  tools:
+    python: "3.9"
+    # You can also specify other tool versions:
+    # nodejs: "16"
+    # rust: "1.55"
+    # golang: "1.17"
+
+# Build documentation in the docs/ directory with Sphinx
+sphinx:
+   configuration: docs/conf.py
+
+# If using Sphinx, optionally build your docs in additional formats such as PDF
+# formats:
+#    - pdf
+
+# Optionally declare the Python requirements required to build your docs
+#python:
+#   install:
+#   - requirements: docs/requirements.txt

docs/customization.rst

@@ -32,6 +32,30 @@ This is demonstrated in the `memory-fixed
   the :py:class:`DefaultGenomeConfig <genome.DefaultGenomeConfig>` implementation; different genome implementations
   may require a different method of registration.
 
+.. index:: aggregation function
+
+New aggregation functions
+-------------------------
+New :term:`aggregation functions <aggregation function>` are registered with your :py:class:`Config <config.Config>` instance, prior to creation of the
+:py:class:`Population <population.Population>` instance, as follows::
+
+    def l2norm(x):
+        return sqrt(sum(i**2 for i in x))
+
+    config.genome_config.add_activation('my_l2norm_function', l2norm)
+
+The first argument to :py:meth:`add_activation <genome.DefaultGenomeConfig.add_aggregation>` is the name by which this aggregation function will be referred to in the configuration settings file.
+
+This is demonstrated in the `memory-fixed
+<https://github.com/CodeReclaimers/neat-python/tree/master/examples/memory-fixed>`_ example.
+
+.. note::
+
+  This method is only valid when using the :py:class:`DefaultGenome <genome.DefaultGenome>` implementation, with the method being found in
+  the :py:class:`DefaultGenomeConfig <genome.DefaultGenomeConfig>` implementation; different genome implementations
+  may require a different method of registration.
+
+
 .. index:: reporting
 
 Reporting/logging

examples/memory-fixed/config

@@ -16,16 +16,16 @@ initial_connection      = partial_direct 0.5
 feed_forward            = False
 compatibility_disjoint_coefficient = 1.0
 compatibility_weight_coefficient   = 0.6
-conn_add_prob           = 0.1
-conn_delete_prob        = 0.1
-node_add_prob           = 0.1
-node_delete_prob        = 0.1
+conn_add_prob           = 0.2
+conn_delete_prob        = 0.2
+node_add_prob           = 0.2
+node_delete_prob        = 0.2
 activation_default      = my_sinc_function
 activation_options      = sigmoid my_sinc_function
 activation_mutate_rate  = 0.1
 aggregation_default     = sum
-aggregation_options     = sum
-aggregation_mutate_rate = 0.0
+aggregation_options     = sum my_l2norm_function
+aggregation_mutate_rate = 0.1
 bias_init_mean          = 0.0
 bias_init_stdev         = 1.0
 bias_replace_rate       = 0.1
@@ -52,13 +52,13 @@ enabled_default         = True
 enabled_mutate_rate     = 0.01
 
 [DefaultSpeciesSet]
-compatibility_threshold = 3.0
+compatibility_threshold = 3.1
 
 [DefaultStagnation]
-species_fitness_func = max
+species_fitness_func = mean
 max_stagnation  = 20
 
 [DefaultReproduction]
-elitism            = 2
-survival_threshold = 0.2
+elitism            = 5
+survival_threshold = 0.3
 

examples/memory-fixed/evolve.py

@@ -7,7 +7,6 @@
 """
 
 import math
-import multiprocessing
 import os
 import random
 
@@ -24,6 +23,15 @@ def sinc(x):
     return 1.0 if x == 0 else math.sin(x) / x
 
 
+# Demonstration of how to add your own custom aggregation function.
+# This l2norm function will be available if my_l2norm_function is included in the
+# config file aggregation_options option under the DefaultGenome section.
+# Note that l2norm is not necessarily useful for this example, it was chosen
+# arbitrarily just to demonstrate adding a custom aggregation function.
+def l2norm(x):
+    return (sum(i**2 for i in x))**0.5
+
+
 # N is the length of the test sequence.
 N = 4
 # num_tests is the number of random examples each network is tested against.
@@ -74,13 +82,17 @@ def run():
     # config file activation_options option under the DefaultGenome section.
     config.genome_config.add_activation('my_sinc_function', sinc)
 
+    # Demonstration of how to add your own custom aggregation function.
+    # This l2norm function will be available if my_l2norm_function is included in the
+    # config file aggregation_options option under the DefaultGenome section.
+    config.genome_config.add_aggregation('my_l2norm_function', l2norm)
+
     pop = neat.Population(config)
     stats = neat.StatisticsReporter()
     pop.add_reporter(stats)
     pop.add_reporter(neat.StdOutReporter(True))
 
-    pe = neat.ParallelEvaluator(multiprocessing.cpu_count(), eval_genome)
-    winner = pop.run(pe.evaluate, 1000)
+    winner = pop.run(eval_genomes, 200)
 
     # Log statistics.
     stats.save()