Metadata-Version: 2.1
Name: genetist
Version: 0.9.8
Summary: Genetist: optimization with genetic algorithms
Home-page: https://github.com/unaiLopez/genetist
Author: Unai Lopez Ansoleaga
Author-email: unai19970315@gmail.com
License: MIT
Description: ![alt text](https://github.com/unaiLopez/darwin/blob/master/doc/images/darwin.jpg?raw=true)
        
        # Genetist: A genetic algorithm powered hyperparameter optimization framework
        [![Python](https://img.shields.io/badge/python-3.6%20%7C%203.7%20%7C%203.8%20%7C%203.9%20%7C%203.10-blue)](https://www.python.org)
        [![pypi](https://img.shields.io/pypi/v/genetist.svg)](https://pypi.python.org/pypi/genetist)
        [![License](https://img.shields.io/badge/License-MIT-blue.svg)](https://opensource.org/licenses/MIT)
        
        Genetist is a high level framework that helps optimizing functions using the power of genetic algorithms.
        
        ## Installation
        Genetist is available at [PyPI](https://pypi.org/project/genetist/)
        ```
        $ pip install genetist
        ```
        ## Quickstart
        
        ```python
        from genetist.environment import Environment
        
        #defining a 4 variable search space of float values from -100.0 to 100.0
        params = {
            'x': {'low': -100.0, 'high': 100.0},
            'y': {'low': -100.0, 'high': 100.0},
            'z': {'low': -100.0, 'high': 100.0},
            'k': {'low': -100.0, 'high': 100.0}
        }
        
        #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
        
        if __name__ == '__main__':
            #defining our Environment instance with a population of 1000 individuals, 250 generation,
            #one-point crossover and a single gene mutation with a 25% probability of mutation
            environment = Environment(
                params=params,
                num_population=1000,
                generations=250,
                crossover_type='one_point',
                mutation_type='single_gene',
                prob_mutation=0.25,
                verbose=0
            )
        
            #minimizing the objective function
            results = environment.optimize(objective=objective, direction='minimize')
        
            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)
        ```
Platform: UNKNOWN
Requires-Python: >=3.6
Description-Content-Type: text/markdown
