Metadata-Version: 2.1
Name: PyGAopt
Version: 1.0.0
Summary: A Python Genetic Algorithm Library.
Home-page: https://github.com/danielkelshaw/PyGA
Author: Daniel Kelshaw
Author-email: daniel.j.kelshaw@gmail.com
License: MIT License
Description: # PyGA
        
        [![Build Status](https://travis-ci.org/danielkelshaw/PyGA.svg?branch=master)](https://travis-ci.org/danielkelshaw/PyGA)
        
        PyGA is an extensible toolkit for Genetic Algorithms (GA) in Python.
        
        The library aims to provide a high-level declarative interface which
        ensures that GAs can be implemented and customised with ease. PyGA 
        features an extensible framework which allows researchers to provide 
        custom implementations which interface with existing functionality.
        
        - **License:** MIT
        - **Python Versions:** 3.6+
        
        ## **Features:**
        - [x] High-level module for Genetic Algorithms.
        - [x] Extensible API for implementing new functionality.
        
        ## **Basic Usage:**
        
        PyGA aims to provide a high-level interface for Genetic Algorithms - the
        code below demonstrates just how easy running an optimisation procedure
        can be.
        
        ```python
        import pyga
        from pyga.utils.functions import single_objective as fx
        
        
        bounds = {
            'x0': [-1e6, 1e6],
            'x1': [-1e6, 1e6],
            'x2': [-1e6, 1e6]
        }
        
        optimiser = pyga.SOGA(bounds, n_individuals=30, n_iterations=100)
        optimiser.optimise(fx.sphere)
        ```
        
        ## **History:**
        The optimisation history is written to a ```History``` data structure
        to allow the user to further investigate the optimisation procedure 
        upon completion. This is a powerful tool, letting the user define custom
        history classes which can record whichever data the user desires.
        
        Tracking the history of the optimisation process allows for plotting
        of the results, an example demonstration is seen in the
        ```plot_fitness_history``` function - this can be further customised
        through the designation of a ```PlotDesigner``` object which provides
        formatting instructions for the graphing tools.
        
        ## **Constraints:**
        
        PyGA  allows the user to define a set of constraints for the 
        optimisation problem - this is achieved through inheriting a template 
        class and implementing the designated method. An example of which is 
        demonstrated below:
        
        ```python
        from pyga.constraints.base_constraints import PositionConstraint
        
        
        class UserConstraint(PositionConstraint):
        
            def constrain(self, position):
                return position['x0'] > 0 and position['x1'] < 0
        
        
        optimiser.constraint_manager.register_constraint(UserConstraint())
        ```
        
        This provides the user with a large amount of freedom to define the
        appropriate constraints and allows the `ConstraintManager` to deal with
        the relevant constraints at the appropriate time.
        
        ## **Customisation:**
        Though the base ```SOGA``` will work for many, there maybe aspects that
        one may want to change, such as the selection / recombination methods.
        A common interface has been designed for these, this ensures that the
        user can alter the functionality at will and researchers can implement
        additional functionality with ease.
        
        Attributes of the ```SOGA``` instance can be modified to implement
        alternative methods, this is demonstrated below:
        
        ```python
        # using 'uniform crossover' as the crossover method
        from pyga.utils.crossovers import UniformCrossover
        optimiser.crossover = UniformCrossover(p_swap=0.25)
        ```
        ```python
        # using 'fitness-proportionate selection' as the selection method
        from pyga.utils.selections import FitnessProportionateSelection
        optimiser.selection = FitnessProportionateSelection()
        ```
        
        It is also possible to define alternative termination criteria through
        implementation of a ```TerminationManager``` class, a couple of examples
        are demonstrated below:
        
        ```python
        # using elapsed time as the termination criteria
        from pyga.utils.termination_manager import TimeTerminationManager
        optimiser.termination_manager = TimeTerminationManager(t_budget=10_000)
        ```
        
        ```python
        # using error as the termination criteria
        from pyga.utils.termination_manager import ErrorTerminationManager
        optimiser.termination_manager = ErrorTerminationManager(
            optimiser, target=0.0, threshold=1e-3
        )
        ```
        
        ###### Author: Daniel Kelshaw
Platform: UNKNOWN
Description-Content-Type: text/markdown
