genetist 0.9.10

Genetist: optimization with genetic algorithms

Genetist: A genetic algorithm powered hyperparameter optimization framework

Genetist is a high level framework that helps optimizing functions using the power of genetic algorithms.

1. Installation

Genetist is available at PyPI

$ pip install genetist

2. Quickstart

2.1. Define Search Space

2.1.1. Fixed Search Space

#defining a fixed set of params for 4 variables
params = {
    'x': [35, -51, 0, 1, 2, 3, 4, 66, 11, 50, 90],
    'y': [-100, -51, 0, 7, 32, 31, 4, 51, 121, 50, 90, 1000, 231]
    'z': [-10, -51, 0, 12, 2, 43, 43, 5, 1231, 50, 90],
    'k': [-56, -51, 0, 1, 2, 13, 4, 5, 11, 50, 90]
}

2.1.2. Flexible Search Space

from genetist.params import Params

#defining a 4 variable search space of float values from -100.0 to 100.0
params = {
    'x': Params.suggest_float(-100, 100),
    'y': Params.suggest_float(-100, 100),
    'z': Params.suggest_float(-100, 100),
    'k': Params.suggest_float(-100, 100)
}

2.2. Define Objective Function

#defining an objective function
def objective(individual):
    x = individual['x']
    y = individual['y']
    z = individual['z']
    k = individual['k']

    return (x**2 - 4*y**3 / z**4) * k**3

2.3. Start Optimization

from genetist.environment import Environment

if __name__ == '__main__':
    #defining our Environment instance with a population of 100 individuals,
    #one-point crossover and a single gene mutation with a 25% probability of mutation
    environment = Environment(
        params=params,
        num_population=100,
        crossover_type='one-point',
        mutation_type='single-gene',
        prob_mutation=0.25,
        verbose=1
    )
    #minimizing the objective function and adding 3 stop criterias (num_generations, timeout, stop_score)
    results = environment.optimize(objective=objective, direction='minimize', num_generations=9999, timeout=60, stop_score=-np.inf)

2.4. Show Optimization Results

print()
print(f'EXECUTION TIME={results.execution_time}')
print(f'BEST SCORE={results.best_score}')
print(f'BEST INDIVIDUAL={results.best_individual}')
print('BEST PER GENERATION:')
print(results.best_per_generation_dataframe)