geneticengine.algorithms.gp.parameterless
=========================================

.. py:module:: geneticengine.algorithms.gp.parameterless


Attributes
----------

.. autoapisummary::

   geneticengine.algorithms.gp.parameterless.T


Classes
-------

.. autoapisummary::

   geneticengine.algorithms.gp.parameterless.ParameterlessPopulationInitializer
   geneticengine.algorithms.gp.parameterless.RandomizeParallelStep
   geneticengine.algorithms.gp.parameterless.RegenerateWeightsStep
   geneticengine.algorithms.gp.parameterless.GenericAdaptiveCrossoverStep
   geneticengine.algorithms.gp.parameterless.InitiallyRandomGeneticProgramming
   geneticengine.algorithms.gp.parameterless.AlwaysRandomGeneticProgramming


Functions
---------

.. autoapisummary::

   geneticengine.algorithms.gp.parameterless.generate_random_population_size
   geneticengine.algorithms.gp.parameterless.time_for_initialization
   geneticengine.algorithms.gp.parameterless.best_of_population


Module Contents
---------------

.. py:function:: generate_random_population_size(random)

.. py:function:: time_for_initialization(budget)

.. py:class:: ParameterlessPopulationInitializer(budget, tracker)

   Bases: :py:obj:`geneticengine.algorithms.gp.structure.PopulationInitializer`


   .. py:attribute:: budget


   .. py:attribute:: tracker


   .. py:method:: initialize(problem, representation, random, target_size, **kwargs)


.. py:class:: RandomizeParallelStep

   Bases: :py:obj:`geneticengine.algorithms.gp.operators.combinators.ParallelStep`


   .. py:method:: post_iterate(problem, evaluator, representation, random, population, target_size, generation)


.. py:data:: T

.. py:function:: best_of_population(population, problem)

.. py:class:: RegenerateWeightsStep(mut, xo, tmut, txo)

   Bases: :py:obj:`geneticengine.algorithms.gp.operators.combinators.IdentityStep`


   .. py:attribute:: mut


   .. py:attribute:: xo


   .. py:attribute:: tmut


   .. py:attribute:: txo


   .. py:method:: post_iterate(problem, evaluator, representation, random, population, target_size, generation)


.. py:class:: GenericAdaptiveCrossoverStep(probability = 1)

   Bases: :py:obj:`geneticengine.algorithms.gp.operators.crossover.GenericCrossoverStep`


   .. py:attribute:: last_fitness
      :type:  geneticengine.problems.Fitness


   .. py:attribute:: first
      :value: True



   .. py:method:: post_iterate(problem, evaluator, representation, random, population, target_size, generation)


.. py:class:: InitiallyRandomGeneticProgramming(problem, budget, representation, random = None, tracker = None)

   Bases: :py:obj:`geneticengine.algorithms.gp.gp.GeneticProgramming`


   A Genetic Programming version that uses random configurations, set before the evolution.


   .. py:attribute:: population_initializer


   .. py:attribute:: population_size


   .. py:attribute:: mutation_tournament


   .. py:attribute:: mutation


   .. py:attribute:: crossover_tournament


   .. py:attribute:: crossover


   .. py:attribute:: step


.. py:class:: AlwaysRandomGeneticProgramming(problem, budget, representation, random = None, tracker = None)

   Bases: :py:obj:`geneticengine.algorithms.gp.gp.GeneticProgramming`


   A Genetic Programming version that uses random configurations, which are regenerated every generation.


   .. py:attribute:: population_initializer


   .. py:attribute:: population_size


   .. py:attribute:: mutation_tournament


   .. py:attribute:: mutation


   .. py:attribute:: crossover_tournament


   .. py:attribute:: crossover


   .. py:attribute:: step