.. _prebuilt:

Prebuilt Algorithms
===================
.. _figtiers:
.. figure:: _static/tiers.png
    :align: left

    **The three tiers of tailorability for using LEAP.**
    LEAP has three levels of abstraction with gradually increasing order of
    customization.  The top-level has ea_solve() that is ideal for real-valued
    optimization problems.  The mid-level has two functions that allows for some
    tailoring of the pipeline and representation, generational_ea() and
    steady_state().  The bottom-most tier provides maximum flexibility and
    control over an EA's implementation, and involves the practitioner assembling
    bespoke EAs using LEAP low-level components, as shown by the code snippet.

.. This figure is at https://docs.google.com/drawings/d/1VE9p3An0vahFWWuFsE6fHqGyfyPfZfUvhZx3RfPyXNo/edit?usp=sharing

:numref:`figtiers` depicts the top-level entry-point, `ea_solve()`, and has the
least customization, but is ideal for real-valued optimization problems. The
mid-level allows for more user customization. `generational_ea()` allows for
implementing most traditional evolutionary algorithms, such as genetic
algorithms and evolutionary programs. `asynchronous.steady_state()` is used to
implement an asynchronous steady-state EA suitable for HPC platforms as they
make the best use of HPC resources. The bottom-most level provides the
greatest amount of flexability, and is where users implement their
evolutionary algorithms using low-level LEAP components.

`ea_solve()` and `generational_ea()` is documented below.
`asynchronous.steady_state()` is documented in :ref:`asea`.  Information on
the bottom-most tier can be found in :doc:`building`.

.. TODO: explicitly make these two subsections and add ancillary supporting text

`ea_solve()`
------------

.. autofunction:: leap_ec.simple.ea_solve
    :noindex:

Example
^^^^^^^
And example using `ea_solve()` can be found in `examples/simple/simple.py`.

`generational_ea()`
-------------------

.. autofunction:: leap_ec.algorithm.generational_ea
    :noindex:

Example
^^^^^^^
And example using `generational_ea()` can be found in `examples/simple/int_rep.py`.