This is Genetic Algorithm implementation, written in Python. The goal is to create a Genetic Algorithm that is quick to implement and easy to use. This implementation introduces several new features that I came up with while working with it, such as support for stochastic problems and new types offspring creation.
GeneticAlgorithm = GA(
generations_count=100, # number of simulated generations
population_count=200, # number of population in generation
function=function, # function to optimize
params_bounds=bounds, # variable bounds for each argument
fitness_threshold=None, # threshold value of fitness for early stopping
maximize=False, # decide to optimize values for minimizing or maximizing function output
floating_point=True, # variables data type
stochastic=False, # set True if function to optimize have stochastic nature
stochastic_iterations=3, # if function to optimize have stochastic nature, performs multiple calculations for every individual (>=3)
allow_gene_duplication=True, # allows the same genes to be calculated several times
crossover_percentage=0.3,# percentage of population reproduced by crossover
mutation_percentage=0.7 # percentage of population reproduced by mutation
)GeneticAlgorithm.evolve(verbose=1)Runs a solution search. Verbosity mode. 0 = progress bar, 1 = generation number and best stats, 2 = adds console clear between generations.
GeneticAlgorithm.get_best()Returns best solution.
GeneticAlgorithm.get_best_fitness()Returns best solution fitness.
GeneticAlgorithm.get_evolution_time()Returns searching time in seconds.
GeneticAlgorithm.plot_learning_curve(
title = 'Fitness over generations',
xlabel = 'Generation',
ylabel = 'Fitness',
font_size = 12,
line_width = 2,
save_dir = None,
image_name = 'learning_curve.png'
)Plots learning curve and returns fitness array. if save_dir is None, the image will not be saved.
GeneticAlgorithm.get_searched_list()Returns a list of searched solutions, only if allow_gene_duplication=False.
A test of a genetic algorithm on a popular optimization problem, the Rosenbrock function. The script runs a basic optimization using a relatively small population and number of generations.
Sample learning curve:
After the evolution is complete, the searched solutions are plotted on the function plane. The best solution is marked as a big red dot.
