Using the Package
=================

Getting Started
---------------

To get started with this package clone this repo::

    git clone https://github.com/mwolinska/Evolutionary-Optimization

Then enter the correct directory on your machine::

    cd Evolutionary-Optimization

This package uses `poetry <https://python-poetry.org>`_ dependency manager.
To install all dependencies run::

    poetry install

Running Experiments
-------------------
To run the code type the following in your terminal. The default experiment is a
simple optimization of the $x^{2}$ using integers.::

    run_evolution

The parameters used for the run can be edited within the main.py file.::

    genotype_class = Genotype.get_genotype(Genotypes.FLOAT_LIST)
    phenotype_class = Phenotype.get_phenotype(Phenotypes.PARABOLA)
    fitness_function_class = FitnessFunction.get_fitness_function(FitnessFunctions.MINIMIZE)
    fitness_function_instance = fitness_function_class()

    evolutionary_algorithm = Evolution(
        phenotype=phenotype_class(genotype_class()),
        number_of_individuals=10,
        number_of_generations=5,
        fitness_function=fitness_function_instance,
        ratio_of_elite_individuals=0.1
    )

This will produce the following output::

    The value of the best individual is [-0.2240206935262563]

To generate the graph of fitness over time use::

    evolutionary_algorithm.plot_performance()

within the run_evolution function to produce the following output:

.. image:: /../../Images/algorithm_plots/sample_evolution_over_time.png
   :height: 400px
   :align: center

To generate the graph of the phenotype function and best individual phenotype / genotype
pairs, use::

    # generate points to plot the phenotype function
    phenotype_function_points_tuple = generate_points_for_function(
        phenotype=evolutionary_algorithm.population.phenotype,
        bottom_plotting_limit=-10,
        c=10,
    )
    # plot phenotype function and best individual phenotype / genotype pairs
    evolutionary_algorithm.plot_phenotype_function_and_best_individuals(phenotype_function__points_tuple)

To produce the following output:

.. image:: /../../Images/algorithm_plots/phenotype_func_and_best_individuals.png
   :height: 400px
   :align: center

Personalising Experiments
-------------------------
To personalise your experiment you can either use the prebuilt phenotypes and genotypes using our interface,
or you can build your own.
To do so, you simply need to create a new phenotype / genotype class that
inherits from the corresponding abstract class and implement the methods to suit your needs.

The classes structure is outlined in the diagram below

.. image:: /../../Images/code_structure/classes_structure_diagram.svg
   :align: center

The implemented fitness functions are outlined in the diagram below.

.. image:: /../../Images/code_structure/abstract_fitness_functions_diagram.svg
   :align: center