Add basic documentation for EvolvablePlayer

Author: marcharper

docs/tutorials/further_topics/evolvable_players.rst

@@ -0,0 +1,15 @@
+.. _evolvable_players:
+
+Evolvable Players
+=================
+
+Several strategies in the library derive from :code:`EvolvablePlayer` which specifies methods
+allowing evolutionary or particle swarm algorithms to be used with these strategies. The
+`Axelrod Dojo library <https://github.com/Axelrod-Python/axelrod-dojo>`_ [Axelrod1980]_
+contains implementations of both algorithms for use with the Axelrod library. Examples include
+FSMPlayers, ANN (neural networks), and LookerUp and Gambler (lookup tables).
+
+New :code:`EvolvablePlayer` subclasses can be added to the library. Any strategy that can
+define :code:`mutation` and :code:`crossover` methods can be used with the evolutionary algorithm
+and the atomic mutation version of the Moran process. To use the particle swarm algorithms, methods
+to serialize the strategy to and from a vector of floats must be defined.

docs/tutorials/further_topics/index.rst

@@ -18,3 +18,4 @@ Contents:
    ecological_variant.rst
    fingerprinting.rst
    meta_strategies.rst
+   evolvable_players.rst

docs/tutorials/getting_started/moran.rst

@@ -132,3 +132,18 @@ Other types of implemented Moran processes:
 
 - :ref:`moran-process-on-graphs`
 - :ref:`approximate-moran-process`
+
+Atomic Mutation for Evolvable Players
+-------------------------------------
+
+Additionally, the Moran process implementation supports a second style of mutation suitable for
+evolving new strategies. The :code:`EvolvablePlayer` class supplies a method that typically produces
+a similar but mutated strategy. This is in contrast to the above method of mutation that selects
+one of the other player types rather than generating a new player type. To use this mutation style
+set `mutation_method=atomic`
+
+    >>> C = axl.Action.C
+    >>> players = [axl.EvolvableFSMPlayer(num_states=2, initial_state=1, initial_action=C) for _ in range(5)]
+    >>> mp = axl.MoranProcess(players, turns=10, mutation_method="atomic")
+    >>> population = mp.play()  # doctest: +SKIP
+