Thesis-OptimizationModules/docs/source/documentations/operators.rst

6. Apply operators to solution
==============================

Applying an operator to a solution consists of modifying the current state of the solution in order to obtain a new one. The goal is to find a better solution in the search space.

6.1. Operators definition
~~~~~~~~~~~~~~~~~~~~~~~~~

In the discrete optimisation literature, we can categorise operators into two sections:

- **mutators**: modification of one or more elements of a solution from its current state.
- **crossovers**: Inspired by Darwin's theory of evolution, we are going here from two solutions to generate a so-called offspring solution composed of the fusion of the data of the parent solutions.

Inside **Macop**, operators are also decomposed into these two categories. Inside ``macop.operators.discrete.base``, generic class ``Operator`` enables to manage any kind of operator.

.. code-block:: python

    class Operator():
        """
        Abstract Operator class which enables to update solution applying operator (computation)
        """
        @abstractmethod
        def __init__(self):
            pass

        @abstractmethod
        def apply(self, solution):
            """
            Apply the current operator transformation
            """
            pass

        def setAlgo(self, algo):
            """
            Keep into operator reference of the whole algorithm
            """
            self._algo = algo

Like the evaluator, the operator keeps **track of the algorithm** (using ``setAlgo`` method) to which he will be linked. This will allow better management of the way in which the operator must take into account the state of current data relating to the evolution of research.

``Mutation`` and ``Crossover`` classes inherite from ``Operator``. An ``apply`` function is required for any new operator.

.. code-block:: python

    class Mutation(Operator):
        """Abstract Mutation extend from Operator

        Attributes:
            kind: {KindOperator} -- specify the kind of operator
        """
        def __init__(self):
            self._kind = KindOperator.MUTATOR

        def apply(self, solution):
            raise NotImplementedError


    class Crossover(Operator):
        """Abstract crossover extend from Operator

        Attributes:
            kind: {KindOperator} -- specify the kind of operator
        """
        def __init__(self):
            self._kind = KindOperator.CROSSOVER

        def apply(self, solution1, solution2):
            raise NotImplementedError

We will now detail these categories of operators and suggest some relative to our problem.

6.2. Mutator operator
~~~~~~~~~~~~~~~~~~~~~

As detailed, the mutation operator consists in having a minimum impact on the current state of our solution. Here is an example of a modification that could be done for our problem.

.. image:: ../_static/documentation/project_knapsack_mutator.png
   :width:  800 px
   :align: center

In this example we change a bit value randomly and obtain a new solution from our search space.

.. warning::
    Applying an operator can conduct to a new but invalid solution from the search space.

The modification applied here is just a bit swapped. Let's define the ``SimpleBinaryMutation`` operator, allows to randomly change a binary value of our current solution.


.. code-block:: python

    """
    modules imports
    """
    from macop.operators.discrete.base import Mutation

    class SimpleBinaryMutation(Mutation):

        def apply(self, solution):
            
            # obtain targeted cell using solution size
            size = solution._size
            cell = random.randint(0, size - 1)

            # copy of solution
            copy_solution = solution.clone()

            # swicth values
            if copy_solution._data[cell]:
                copy_solution._data[cell] = 0
            else:
                copy_solution._data[cell] = 1

            # return the new obtained solution
            return copy_solution

We can now instanciate our new operator in order to obtain a new solution:


.. code-block:: python

    """
    BinaryMutator instance
    """
    mutator = SimpleBinaryMutation()

    # using defined BinarySolution
    solution = BinarySolution.random(5)

    # obtaining new solution using operator
    new_solution = mutator.apply(solution)


.. note::
    The developed ``SimpleBinaryMutation`` is available into ``macop.operators.discrete.mutators.SimpleBinaryMutation`` in **Macop**.


6.3. Crossover operator
~~~~~~~~~~~~~~~~~~~~~~~


Inspired by Darwin's theory of evolution, crossover starts from two solutions to generate a so-called offspring solution composed of the fusion of the data of the parent solutions.

.. image:: ../_static/documentation/project_knapsack_crossover.png
   :width:  800 px
   :align: center

In this example we merge two solutions with a specific splitting criterion in order to obtain an offspring.

We will now implement the SimpleCrossover crossover operator, which will merge data from two solutions. 
The first half of solution 1 will be saved and added to the second half of solution 2 to generate the new solution (offspring).


.. code-block:: python

    """
    modules imports
    """
    from macop.operators.discrete.base import Crossover

    class SimpleCrossover(Crossover):

        def apply(self, solution1, solution2):
            
            size = solution1._size

            # default split index used
            splitIndex = int(size / 2)

            # copy data of solution 1
            firstData = solution1._data.copy()

            # copy of solution 2
            copy_solution = solution2.clone()

            copy_solution._data[splitIndex:] = firstData[splitIndex:]

            return copy_solution


We can now use the crossover operator created to generate new solutions. Here is an example of use:

.. code-block:: python

    """
    SimpleCrossover instance
    """
    crossover = SimpleCrossover()

    # using defined BinarySolution
    solution1 = BinarySolution.random(5)
    solution2 = BinarySolution.random(5)

    # obtaining new solution using crossover
    offspring = crossover.apply(solution1, solution2)

.. warning::
    The developed ``SimpleCrossover`` is available into ``macop.operators.discrete.crossovers.SimpleCrossover`` in **Macop**.
    However, the choice of halves of the merged data is made randomly. In addition, the second solution can be omitted, 
    by default the operator will crossover between ``solution1`` and the best current solution of the algorithm.

Next part introduce the ``policy`` feature of **Macop** which enables to choose the next operator to apply during the search process based on specific criterion.