baumeva 0.4.3

Library for the solution of optimization problems with evolution algorithms

BaumEvA - Bauman Evolution Algorithm

BaumEvA is an advanced genetic algorithm crafted in Python. It's designed to work with both binary and combinatorial data types, providing a comprehensive suite of tools for optimization and search tasks. Baumeva offers a variety of selection, crossover, mutation, and parent selection mechanisms.

Installation

To install the BaumEvA, run the following command:

pip install baumeva

Usage

Basic Usage

BaumEvA is designed for easy integration and usage. The library features two classes: BinaryGA and CombinatoryGA. These classes come equipped with predefined methods, allowing users to effortlessly engage with both binary and combinatorial genetic algorithms.

from baumeva import BinaryGA

# function to be optimized
def func_grivanka(value_list):
    res_sum = 0
    res_mpl = 1
    for i, value in enumerate(value_list):
        res_sum += (value*value) / 4000
        res_mpl *= math.cos(value/math.sqrt(i+1))
    return res_sum - res_mpl + 1

# simple method for use GA
binary_ga = BinaryGA(num_generations=100,
                     num_individ=100,
                     gens=((-16, 16, 0.01), (-16, 16, 0.01)),
                     obj_function=func_grivanka,
                     obj_value=0)
data = binary_ga.optimize()

The num_generations specifies the total number of generations. In the example given num_generations=100 means the algorithm will iterate through 100 generations.

The num_individ dictates the total number of individuals in a population. In the example given num_individ=100 implies there are 100 individuals in a single generation.

The gens defines the range and precision for each gene in the binary representation. Each inner tuple comprises: minimum value for the gene, maximum value for the gene, precision for the gene's value (the step). In the example given gens=((-16, 16, 0.01), (-16, 16, 0.01)) signifies two genes, both ranging from -16 to 16 with a precision of 0.01.

The obj_function describes the objective function that evaluates the object score of each individual. In the example given obj_function=func_grivanka indicates the func_grivanka function will be used to determine the object score and subsequently fitness score of the individuals.

The obj_value defines the target or optimal value the algorithm aims to achieve or get as close as possible to, using the obj_function. In the example given obj_value=0 means the genetic algorithm will aim to find an individual whose object function value is as close to 0 as possible.

Advanced Usage

Additionally, Baumeva offers a modular approach for those who desire more customization. Beyond the convenience of the BinaryGA and CombinatoryGA classes, users have the flexibility to assemble a genetic algorithm. By directly declaring specific classes, you can handpick from a diverse range of mutation, selection and crossover methods, crafting a customized genetic algorithm.

import random
from baumeva import GaData
from baumeva import OrderCatPopulation
from baumeva import HyperbolaFitness
from baumeva import TournamentSelection
from baumeva import OrderCrossover
from baumeva import InversionMutation
from baumeva import NewGeneration


def word_distance(word):
    return sum(c1 != c2 for c1, c2 in zip('algorithm', word))


def string_generator():
    chars = list('algorithm')
    while True:
        random.shuffle(chars)
        yield ''.join(chars)


generated_population = [next(string_generator()) for _ in range(50)]

ga_data = GaData(num_generations=100, early_stop=15)
ocp = OrderCatPopulation()
ocp.set_params(num_individ=50, gens=(0, 8, 9), input_population=generated_population)
ocp.fill()
ga_data.population = ocp
fitness_func = HyperbolaFitness(obj_function=word_distance, obj_value=0)
fitness_func.execute(ga_data=ga_data)
ga_data.update()

for i in range(ga_data.num_generations):
    selection = TournamentSelection(tournament_size=5)
    selection.execute(ga_data)

    order_cross = OrderCrossover()
    order_cross.execute(ga_data)

    bcm = InversionMutation(0.15)
    bcm.execute(ga_data)

    NewGeneration('best').execute(ga_data)

    fitness_func.execute(ga_data=ga_data)
    ga_data.update()

    if ga_data.num_generation_no_improve >= ga_data.early_stop:
        print(f'Early stopping: {i}')
        break

print(f"Result: {ga_data.best_solution} ")

This example demonstrates the use of the BaumEvA library to search for the word "algorithm" using a categorical genetic algorithm.

Components Used:

1. GaData: Class for holding and managing data related to a genetic algorithm run.
2. OrderCatPopulation: Class for representing ordered categorical population in a genetic algorithm. In this case we set the first population manually using generated_population - a list consisting of words anagrams to 'algorithm'.
3. HyperbolaFitness: Class for calculating fitness value using the hyperbola approach. Object function is specified as a parameter - levenstein distance between the evolved string and the target string 'algorithm'.
4. TournamentSelection: Selection method used, which operates on tournament-based selection logic.
5. OrderCrossover: The crossover mechanism used in the genetic algorithm.
6. InversionMutation: Mutation method, introducing randomness in the population.
7. NewGeneration: Utility for generating new population members based on chosen criteria.

How it works:

1. The genetic algorithm data parameters are initialized, and the initial population is set and filled.
2. The fitness of each individual in the population is calculated based on its distance from the target word 'algorithm'.
3. In each generation:
   • The tournament-based selection method is applied.
   • Pairs of individuals undergo crossover using the order-based crossover mechanism.
   • Mutation is applied to individuals with some probability.
   • A new generation is created based on the best individuals.
   • Fitness is recalculated for the new generation.
4. The genetic algorithm either runs for a predefined number of generations or stops early if there hasn't been an improvement in the population fitness for a specified number of generations.
5. The result is the closest approximation found to the word 'algorithm'.

License

The BaumEvA library is distributed under the MIT License. See the LICENSE file for more information.

Contribution

Contributions are welcome! If you encounter any issues or have suggestions for improvements, please open an issue on the GitHub repository.

Contact

For any inquiries or questions, you can reach out to the author via email at [e-mail].