Metadata-Version: 2.1
Name: geneal
Version: 0.3
Summary: Python Genetic Algorithms library
Home-page: https://github.com/diogomatoschaves/geneal
Author: Diogo Matos Chaves
Author-email: di.matoschaves@gmail.com
License: MIT license
Description: # GeneAl
        
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        `geneal` is a python library implementing genetic algorithms (GAs). It has functionality for both binary and continuous GA,
        as well as specific use case applications such as a solver for the
        [Travelling Salesman Problem](https://en.wikipedia.org/wiki/Travelling_salesman_problem). 
        
        ## Installation
        
        ```
        $ pip install geneal
        ```
        
        ## Usage
        
        `geneal` provides 2 main GA solver classes, `BinaryGenAlgSolver` and `ContinuousGenAlgSolver` 
        for binary and continuous problem formulations respectively. The library is set up in such a way that all problems
        have to be a maximization, and hence, the fitness functions need to be adjusted accordingly.
        
        ### Binary GA
        
        The binary GA class can be customized by the input variables provided on its initialization. 
        The minimum required arguments to provide at the initialization are the number of genes on each individual's
        chromosome, and the fitness function to be maximized. 
        
        An example is shown below.
        
        ```python
        
        from geneal.genetic_algorithms import BinaryGenAlgSolver
        from geneal.applications.fitness_functions.binary import fitness_functions_binary
        
        solver = BinaryGenAlgSolver(
            n_genes=3, # number of variables defining the problem
            fitness_function=fitness_functions_binary(1), # fitness function to be maximized
        )
        
        solver.solve()
        
        ```
        
        The above call will perform the optimization with the default parameters, which will most likely have to be adjusted for
        each individual problem at hand. The algorithm parameters can be customized as shown below.
        
        ```python
        from geneal.genetic_algorithms import BinaryGenAlgSolver
        from geneal.applications.fitness_functions.binary import fitness_functions_binary
        
        solver = BinaryGenAlgSolver(
            n_genes=3,
            fitness_function=fitness_functions_binary(1), 
            n_bits=1, # number of bits describing each gene (variable)
            pop_size=10, # population size (number of individuals)
            max_gen=500, # maximum number of generations
            mutation_rate=0.05, # mutation rate to apply to the population
            selection_rate=0.5, # percentage of the population to select for mating
            selection_strategy="roulette_wheel", # strategy to use for selection. see below for more details
        )
        
        solver.solve()
        
        ```
        
        ### Continuous GA
        
        Following a similar pattern as the binary GA solver, the parameters of the continuous GA can be adjusted at initialization.
        As a bare minimum, the number of variables and the fitness function to maximize must be provided, as shown below.
        
        ```python
        
        from geneal.genetic_algorithms import ContinuousGenAlgSolver
        from geneal.applications.fitness_functions.continuous import fitness_functions_continuous
        
        solver = ContinuousGenAlgSolver(
            n_genes=4, # number of variables defining the problem
            fitness_function=fitness_functions_continuous(3), # fitness function to be maximized
        )
        
        solver.solve()
        
        ```
        
        In order to customize the continuous GA solver further, more arguments can be passed at initialization.
        
        ```python
        
        from geneal.genetic_algorithms import ContinuousGenAlgSolver
        from geneal.applications.fitness_functions.continuous import fitness_functions_continuous
        
        solver = ContinuousGenAlgSolver(
            n_genes=4,
            fitness_function=fitness_functions_continuous(3),
            pop_size=10, # population size (number of individuals)
            max_gen=200, # maximum number of generations
            mutation_rate=0.1, # mutation rate to apply to the population
            selection_rate=0.6, # percentage of the population to select for mating
            selection_strategy="roulette_wheel", # strategy to use for selection. see below for more details
        )
        
        solver.solve()
        
        ```
        
        A notable difference to the binary GA solver is the fact that we can customize the input space of the problem
        by defining if the problem is of type `int` or `float`, and defining an overall minimum and maximum values for each
        variable (or for all at once).
        
        ```python
        
        from geneal.genetic_algorithms import ContinuousGenAlgSolver
        from geneal.applications.fitness_functions.continuous import fitness_functions_continuous
        
        solver = ContinuousGenAlgSolver(
            n_genes=4, 
            fitness_function=fitness_functions_continuous(3),
            pop_size=10,
            max_gen=200,
            mutation_rate=0.1,
            selection_rate=0.6,
            selection_strategy="roulette_wheel",
            problem_type=float, # Defines the possible values as float numbers
            variables_limits=(-10, 10) # Defines the limits of all variables between -10 and 10. 
                                       # Alternatively one can pass an array of tuples defining the limits
                                       # for each variable: [(-10, 10), (0, 5), (0, 5), (-20, 20)]
        )
        
        solver.solve()
        
        ```
        
        ### selection strategy
        
        It is possible to choose the selection strategy that the algorithm will use to select the parents that will 
        be used for generating new offsprings for the next generation. The options are:
        
        - `roulette_wheel`
        - `random`
        - `two_by_two`
        - `tournament`
        
        **roulette wheel**
        
        This selection strategy orders the individuals in the selection pool by probability, with the fittest individuals
        having higher odds of being selected. 
        
        **random**
        
        This selection procedure selects randomly individuals from the selection pool, following in essence a similar procedure
        as the roulette wheel, but with the same probabilities for each individual.
        
        **two_by_two**
        
        This strategy groups the individuals in the mating pool 2 by 2, from top to bottom.
        
        **tournament**
        
        This strategy will select 3 individuals candidates for each parent position, which are then sorted by their fitness
        and from which the fittest one is selected.
        
        ## Specific Applications
        
        In the real world, there's usually the need to adapt a genetic algorithm implementation to each individual problem.
        Thus, `geneal` offers the user a level of customization that aims to be both versatile and relatively simple. For that,
        one just has to create a class which inherits from the `BinaryGenAlgSolver` or `ContinuousGenAlgSolver`
        base classes, and on which some overriding methods are defined. This allows the user to control the main steps of a GA:
        
        - fitness function
        - population initialization
        - mating between individuals (creation of offsprings)
        - mutation of the population
         
        A boilerplate template of such a class is shown below:
        
        ```python
        from geneal.genetic_algorithms import ContinuousGenAlgSolver, BinaryGenAlgSolver
        
        
        class TemplateChildClass(ContinuousGenAlgSolver, BinaryGenAlgSolver):
            def __init__(self, *args, **kwargs):
                BinaryGenAlgSolver.__init__(self, *args, **kwargs)
                ContinuousGenAlgSolver.__init__(self, *args, **kwargs)
        
            def fitness_function(self, chromosome):
                """
                Implements the logic that calculates the fitness
                measure of an individual.
        
                :param chromosome: chromosome of genes representing an individual
                :return: the fitness of the individual
                """
                pass
        
            def initialize_population(self):
                """
                Initializes the population of the problem
        
                :param pop_size: number of individuals in the population
                :param n_genes: number of genes representing the problem. In case of the binary
                solver, it represents the number of genes times the number of bits per gene
                :return: a numpy array with a randomized initialized population
                """
                pass
        
            def create_offspring(
                self, first_parent, sec_parent, crossover_pt, offspring_number
            ):
                """
                Creates an offspring from 2 parents. It uses the crossover point(s)
                to determine how to perform the crossover
        
                :param first_parent: first parent's chromosome
                :param sec_parent: second parent's chromosome
                :param crossover_pt: point(s) at which to perform the crossover
                :param offspring_number: whether it's the first or second offspring from a pair of parents.
                Important if there's different logic to be applied to each case.
                :return: the resulting offspring.
                """
                pass
        
            def mutate_population(self, population, n_mutations):
                """
                Mutates the population according to a given user defined rule.
        
                :param population: the population at a given iteration
                :param n_mutations: number of mutations to be performed. This number is 
                calculated according to mutation_rate, but can be adjusted as needed inside this function
                :return: the mutated population
                """
                pass
        
        ```
        
        ### Travelling Salesman Problem
        
        One of the possible applications of genetic algorithms is to the 
        [Travelling Salesman Problem](https://en.wikipedia.org/wiki/Travelling_salesman_problem). This problem is NP hard,
        as the number of possible solutions grows with the factorial of the number of variables, and therefore, genetic algorithms 
        are a good fit for approximating solutions to these problems.
        
        As this particular problem has its own sets of constraints, a specific class adapted to this problem is provided
        in `TravellingSalesmanProblemSolver`, which can be used out of the box to virtually
        all problems of this kind. As a minimum input, this class requires the user to provide a 
        [networkx](https://networkx.github.io/documentation/stable/) undirected graph containing all the nodes
        representing the problem and with each node connected to every other node by edges with the respective 
        associated cost (weight). To build this graph, one can use the `create_graph` method, as shown below:
        
        ```python
        import turf
        
        from geneal.applications.tsp.helpers import create_graph
        from geneal.applications.tsp.examples.world_capitals import world_capitals_dict
        
        G = create_graph(
            world_capitals_dict, # a python dictionary containing the nodes as keys
            turf.distance, # function to use to calculate distance between nodes
            lon=lambda x: x["CapitalLongitude"], # lambda function on how to retrieve the longitude
            lat=lambda x: x["CapitalLatitude"], # lambda function on how to retrieve the latitude
        )
        
        ```
        
        After having a built graph, one can pass it directly to `TravellingSalesmanProblemSolver`, and the number of genes
        will be automatically retrieved from the number of nodes in the graph.
        
        ```python
        from geneal.applications.tsp.travelling_salesman_problem import TravellingSalesmanProblemSolver
        from geneal.applications.tsp.examples.world_capitals.graph import G
        
        tsp_solver = TravellingSalesmanProblemSolver(graph=G)
        ```
        
        Again, one can control the solver parameters as before, by providing them on the initialization:
        
        ```python
        from geneal.applications.tsp.travelling_salesman_problem import TravellingSalesmanProblemSolver
        from geneal.applications.tsp.examples.world_capitals.graph import G
        
        tsp_solver = TravellingSalesmanProblemSolver(
            graph=G,
            pop_size=10, # population size (number of individuals)
            max_gen=500, # maximum number of generations
            mutation_rate=0.05, # mutation rate to apply to the population
            selection_rate=0.5, # percentage of the population to select for mating
            selection_strategy="tournament", # strategy to use for selection.
            mutation_strategy="random_inversion" # strategy to use for mutation. see below for more details.
        )
        ```
        
        **specific TSP parameters**
        
        Being a particular use case for genetic algorithms, the Travelling Salesman Problem has also specific settings that
        allow the user to control the convergence for each use case. One of such parameters is `mutation_strategy`, which 
        can be one of the following:
        
        - `2-opt`,
        - `random_swap`,
        - `random_inversion`,
        - `random_gene_nearest_neighbour`,
        - `worst_gene_random`,
        - `worst_gene_nearest_neighbour`,
        - `select_any_mutation`,
        
        Below some brief explanation is given on each of the options:
        
        **2-opt**
        
        It performs a 2-opt mutation on a tour. It selects randomly 2 edges from the tour and combines the resulting sub tours
        by swapping them. More details can be found [here](https://en.wikipedia.org/wiki/2-opt).
        
        **random swap**
        
        A random swap mutation will choose randomly 2 genes from a given tour and swap them around. This is the
        kind of mutation that most closely resembles a typical genetic algorithm mutation.
        
        **random inversion**
        
        A random inversion swap will choose a consecutive subset from the tour and reverse it. The size of the subset is also
        chosen randomly. 
        
        **random gene nearest neighbour**
        
        This is a knowledge-based mutation, where a randomly selected gene is shifted next to its closest neighbour. 
        More details can be found in point [4.2.7](https://arxiv.org/pdf/1801.02827.pdf).
        
        **worst gene random mutation**
        
        This is again a knowledge-based mutation, where a randomly selected gene is swapped with the gene contributing 
        most to the cost of the tour. More details can be found in point [4.2.3](https://arxiv.org/pdf/1801.02827.pdf).
        
        **worst gene random mutation**
        
        This is again a knowledge-based mutation, where gene contributing most to the total cost of the tour
        is moved next to one of its neighbours. More details can be found in point [4.2.4](https://arxiv.org/pdf/1801.02827.pdf)
        
Keywords: genetic algorithms,ga,optimization,genetic programming
Platform: UNKNOWN
Classifier: Programming Language :: Python :: 3
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: OS Independent
Requires-Python: >=3.5
Description-Content-Type: text/markdown
