.. DO NOT EDIT.
.. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY.
.. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE:
.. "auto_examples/example_reorder.py"
.. LINE NUMBERS ARE GIVEN BELOW.

.. only:: html

    .. note::
        :class: sphx-glr-download-link-note

        Click :ref:`here <sphx_glr_download_auto_examples_example_reorder.py>`
        to download the full example code

.. rst-class:: sphx-glr-example-title

.. _sphx_glr_auto_examples_example_reorder.py:


Example for evolutionary regression with genome reordering
===========================================================

Example demonstrating the effect of genome reordering.

References
-----------
Goldman B.W., Punch W.F. (2014): Analysis of Cartesian Genetic Programming’s
Evolutionary Mechanisms
DOI: 10.1109/TEVC.2014.2324539

.. GENERATED FROM PYTHON SOURCE LINES 13-34

.. code-block:: default


    # The docopt str is added explicitly to ensure compatibility with
    # sphinx-gallery.
    docopt_str = """
       Usage:
         example_reorder.py [--max-generations=<N>]

       Options:
         -h --help
         --max-generations=<N>  Maximum number of generations [default: 300]
    """

    import matplotlib.pyplot as plt
    import numpy as np
    import scipy.constants
    from docopt import docopt

    import cgp

    args = docopt(docopt_str)








.. GENERATED FROM PYTHON SOURCE LINES 35-36

We first define a target function.

.. GENERATED FROM PYTHON SOURCE LINES 36-42

.. code-block:: default



    def f_target(x):
        return x ** 2 + 1.0









.. GENERATED FROM PYTHON SOURCE LINES 43-47

Then we define the objective function for the evolution. It uses
the mean-squared error between the output of the expression
represented by a given individual and the target function evaluated
on a set of random points.

.. GENERATED FROM PYTHON SOURCE LINES 47-71

.. code-block:: default



    def objective(individual):
        if not individual.fitness_is_None():
            return individual

        n_function_evaluations = 1000

        np.random.seed(1234)

        f = individual.to_func()
        loss = 0
        for x in np.random.uniform(-4, 4, n_function_evaluations):
            # the callable returned from `to_func` accepts and returns
            # lists; accordingly we need to pack the argument and unpack
            # the return value
            y = f(x)
            loss += (f_target(x) - y) ** 2

        individual.fitness = -loss / n_function_evaluations

        return individual









.. GENERATED FROM PYTHON SOURCE LINES 72-75

Next, we set up the evolutionary search. We first define the
parameters for the population, the genome of individuals, and two
evolutionary algorithms without (default) and with genome reordering.

.. GENERATED FROM PYTHON SOURCE LINES 75-98

.. code-block:: default



    population_params = {"n_parents": 1, "seed": 818821}

    genome_params = {
        "n_inputs": 1,
        "n_outputs": 1,
        "n_columns": 12,
        "n_rows": 1,
        "levels_back": None,
        "primitives": (cgp.Add, cgp.Sub, cgp.Mul, cgp.ConstantFloat),
    }

    ea_params = {"n_offsprings": 4, "mutation_rate": 0.03, "n_processes": 2}
    ea_params_with_reorder = {
        "n_offsprings": 4,
        "mutation_rate": 0.03,
        "n_processes": 2,
        "reorder_genome": True,
    }

    evolve_params = {"max_generations": int(args["--max-generations"]), "termination_fitness": 0.0}








.. GENERATED FROM PYTHON SOURCE LINES 99-100

We create two populations that will be evolved

.. GENERATED FROM PYTHON SOURCE LINES 100-103

.. code-block:: default

    pop = cgp.Population(**population_params, genome_params=genome_params)
    pop_with_reorder = cgp.Population(**population_params, genome_params=genome_params)








.. GENERATED FROM PYTHON SOURCE LINES 104-105

and two instances of the (mu + lambda) evolutionary algorithm

.. GENERATED FROM PYTHON SOURCE LINES 105-108

.. code-block:: default

    ea = cgp.ea.MuPlusLambda(**ea_params)
    ea_with_reorder = cgp.ea.MuPlusLambda(**ea_params_with_reorder)








.. GENERATED FROM PYTHON SOURCE LINES 109-110

We define two callbacks for recording of fitness over generations

.. GENERATED FROM PYTHON SOURCE LINES 110-126

.. code-block:: default

    history = {}
    history["fitness_champion"] = []


    def recording_callback(pop):
        history["fitness_champion"].append(pop.champion.fitness)


    history_with_reorder = {}
    history_with_reorder["fitness_champion"] = []


    def recording_callback_with_reorder(pop):
        history_with_reorder["fitness_champion"].append(pop.champion.fitness)









.. GENERATED FROM PYTHON SOURCE LINES 127-128

and finally perform the evolution of the two populations

.. GENERATED FROM PYTHON SOURCE LINES 128-142

.. code-block:: default

    cgp.evolve(
        pop, objective, ea, **evolve_params, print_progress=True, callback=recording_callback,
    )

    cgp.evolve(
        pop_with_reorder,
        objective,
        ea_with_reorder,
        **evolve_params,
        print_progress=True,
        callback=recording_callback_with_reorder,
    )






.. rst-class:: sphx-glr-script-out

 Out:

 .. code-block:: none


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.. GENERATED FROM PYTHON SOURCE LINES 143-145

After finishing the evolution, we plot the evolution of the fittest individual
with and without genome reordering

.. GENERATED FROM PYTHON SOURCE LINES 145-162

.. code-block:: default



    width = 9.0
    fig = plt.figure(1, figsize=[width, width / scipy.constants.golden])

    plt.plot(history["fitness_champion"], label="Champion")
    plt.plot(history_with_reorder["fitness_champion"], label="Champion with reorder")

    plt.xlabel("Generation")
    plt.ylabel("Fitness")
    plt.legend(["Champion", "Champion with reorder"])

    plt.yscale("symlog")
    plt.ylim(-1.0e2, 0.1)
    plt.axhline(0.0, color="0.7")

    fig.savefig("example_reorder.pdf", dpi=300)



.. image:: /auto_examples/images/sphx_glr_example_reorder_001.png
    :alt: example reorder
    :class: sphx-glr-single-img






.. rst-class:: sphx-glr-timing

   **Total running time of the script:** ( 0 minutes  3.935 seconds)


.. _sphx_glr_download_auto_examples_example_reorder.py:


.. only :: html

 .. container:: sphx-glr-footer
    :class: sphx-glr-footer-example



  .. container:: sphx-glr-download sphx-glr-download-python

     :download:`Download Python source code: example_reorder.py <example_reorder.py>`



  .. container:: sphx-glr-download sphx-glr-download-jupyter

     :download:`Download Jupyter notebook: example_reorder.ipynb <example_reorder.ipynb>`


.. only:: html

 .. rst-class:: sphx-glr-signature

    `Gallery generated by Sphinx-Gallery <https://sphinx-gallery.github.io>`_