Metadata-Version: 2.1
Name: simplega
Version: 1.0.1
Summary: A simple implementation of Genetic Algorithm
Home-page: https://github.com/nu12/simplega
Author: Alysson A Costa
Author-email: alysson.avila.costa@gmail.com
License: MIT
Description: # simplega
        
        ```simplega``` is a simple python implementation of genetic algorithm and it is available through PyPI.
        
        ## Install
        ```shell
        python3 -m pip install simplega
        ```
        
        ## Usage
        
        Import the package
        ```python
        from simplega import Chromosome, Population, GA, GAHelper
        # or
        from simplega import *
        ```
        
        Create a fitness function that suits toyr problem
        ```python
        def maximize(chromosome):
            return sum( [ ord(gene) for gene in chromosome.dna ] )
        ```
        
        Create a new instance of GA specifying the fitness function to be used
        ```python
        ga = GA(maximize)
        ```
        
        Perform the steps of the genetic algorithm and retrieve the fittest chromosome
        ```python
        ga.run()
        print(ga.get_fittest())
        ```
        
        All the script - really simple:
        ```python
        from simplega import *
        
        def maximize(chromosome):
            return sum( [ ord(gene) for gene in chromosome.dna ] )
            
        ga = GA(maximize)
        ga.run()
        print(ga.get_fittest())
        ```
        
        ### Advanced usage
        
        You can customize your instance of GA, replacing any or all of its default values
        ```python
        ga = GA(fitness_function, 
          genes =  [ chr(n) for n in range(65,91) ], 
          chromosome_size =  10, 
          population_size =  100, 
          generations =  100, 
          crossover_points =  1, 
          elitism_rate =  0.05, 
          crossover_rate =  0.85, 
          mutation_rate =  0.01, 
          )
        ```
        
        You can print the fittest chromosome of each generation with ```ga.run(True)```
        
        ## Contributing
        
        Please submit bugfixes, enhancements, unit tests, usecases and examples with a pull request.
Platform: UNKNOWN
Classifier: Programming Language :: Python :: 3
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: OS Independent
Description-Content-Type: text/markdown
