Evolutionary Algorithm (EA) 1
=============================

Solve a :ref:`small system <small-test-system>` with Evolutionary Algorithmes. Use decision encoded as place IDs. We use python module `DEAP <https://github.com/deap/deap>`_. See `DEAP documentation <https://deap.readthedocs.io/en/master/overview.html>`_ for more information.


For this tutorial we need following modules

.. literalinclude:: ../../examples/use-evo.py
    :start-at: import copy
    :end-at: import prp.solvers.evo_simple as evo_simple

Parameters
----------

We define paths to JSON files of a small warehouse system

.. literalinclude:: ../../examples/use-evo.py
    :start-at: LAYOUT_FILE = "../data/10-layout.json"
    :end-at: DEPARTURES_FILE = "../data/10-departures.json"

and store the solution to a JSON file:

.. literalinclude:: ../../examples/use-evo.py
    :start-at: SOLUTION_FILE =
    :lines: 1

We also store intermediate results to an CSV file. Set it to None if you do not want to create the CSV file.

.. literalinclude:: ../../examples/use-evo.py
    :start-at: CSV_FILE =
    :lines: 1


We set parameters for the evolutionary algorithm. The evolutionary algorithms are random, use **random.seed(...)** to make results reproducible. (I am not sure if the results are reproducible with a multiprocess solver.)

.. literalinclude:: ../../examples/use-evo.py
    :start-at: random.seed(7)
    :end-at: MAX_NO_IMPROVEMENTS =

Calculate
---------

Load a warehouse model from JSON files.

.. literalinclude:: ../../examples/use-evo.py
    :start-at: def load_problem():
    :end-at: warehouse = load_problem()

Use helper class *evo.Helper* which translates between the Pod Reposition Problems and an Evolutionary Problem.

* It create initial solutions.

* It evaluates individuals from Evolutionary Problem as solutions of a Pod Reposition Problem.


An evolutionary algorithm maximizes fitness of individuals, but we want to minimize costs of individuals. To minimize the costs we define the fitness as *FitnessMin* := (-1)*costs.

.. literalinclude:: ../../examples/use-evo.py
    :start-at: creator.create("FitnessMin",
    :lines: 1

Our individuals are sequence of unsigned integers (type = "L"), whose fitness is *FitnessMin*.

.. literalinclude:: ../../examples/use-evo.py
    :start-at: creator.create("Individual",
    :lines: 1

Setup DEAP solver.

.. literalinclude:: ../../examples/use-evo.py
    :start-at: toolbox = base.Toolbox()
    :lines: 1

Create initial population from a single random solution.

.. literalinclude:: ../../examples/use-evo.py
    :start-at: initial_solution = helper.random_solution()
    :end-at: toolbox.register("population",

Add rules for mating. Use two-point random crossover.

.. literalinclude:: ../../examples/use-evo.py
    :start-at: toolbox.register("mate",
    :lines: 1

Add rules for mutation. Select uniform mutation in such a way that on average there is 3 mutation in an individual.

.. literalinclude:: ../../examples/use-evo.py
    :start-at: toolbox.register("mutate",
    :lines: 1-2

Use helper.evaluate() to calculate fitness. This method returns average costs.

.. literalinclude:: ../../examples/use-evo.py
    :start-at: toolbox.register("evaluate", helper.evaluate)
    :lines: 1

Use tournament based selection. That means, 100 times select random 3 and add the best one to the new population.

.. literalinclude:: ../../examples/use-evo.py
    :start-at: toolbox.register("select", tools.selTournament, tournsize=3)
    :lines: 1

Before we start optimization, we can print out the initial solution and its fitness (=costs).

.. literalinclude:: ../../examples/use-evo.py
    :start-at: ind1 = toolbox.individual()
    :end-at: print("Initial fitness: {}"

Generate initial population with *POPULATION_SIZE* individuals.

.. literalinclude:: ../../examples/use-evo.py
    :start-at: pop = toolbox.population(n=POPULATION_SIZE)
    :lines: 1

Store the best individual after each new population. At the end the hall of fame will contain only one individual with the best fitness.

.. literalinclude:: ../../examples/use-evo.py
    :start-at: hof = tools.HallOfFame(1)
    :lines: 1

Store some population statistics.

.. literalinclude:: ../../examples/use-evo.py
    :start-at: stats = tools.Statistics
    :end-at: stats.register("Max", numpy.max)

If you want to calculate how frequently the *helper* will called and how frequently it will produce infeasible solution, reset its counter before start the evolutionary algorithm.

.. literalinclude:: ../../examples/use-evo.py
    :start-at: helper.reset_counters()
    :lines: 1

Optionally switch on multiprocessing.

.. literalinclude:: ../../examples/use-evo.py
    :start-at: pool = multiprocessing.Pool()
    :lines: 1-2

Start calculation.

.. literalinclude:: ../../examples/use-evo.py
    :start-at: pop, logbook = evo_simple.eaSimple(pop
    :lines: 1-3

Store intermediate results to *CSV_FILE*.

.. literalinclude:: ../../examples/use-evo.py
    :start-at: if CSV_FILE is not None:
    :end-at: csvfile.close()

Print out the best solution and save it to a JSON file.

.. literalinclude:: ../../examples/use-evo.py
    :start-at: print(hof)
    :end-at: recorder.store_solution_to_json
    :lines: 1-3,6-8