Genetic is a python library for easily implementing genetic algorithms
To use genetic, first import the GeneticAlgorithm class and create a subclass:
from genetic import GeneticAlgorithm
class GenAlgSubclass (GeneticAlgorithm):
def __init__(self):
super(GenAlgSubclass, self).__init__()
Your subclass needs to implement two functions:
def genomeSize(self):
""" Defines the number of genomes in an organisms's genome
This function must be overrided.
Returns:
int: The number of genes in an organism's genome. >= 0
"""
# Your implemenetation here...
@abstractmethod
def heuristic(self, organism):
""" Tests the fitness of a specific organism
This function must be overrided. The genetic algorithms seeks to
maximize this function
Arguments:
organism (Organism): The organism to be scored
Returns:
float: The fitness of the organism
"""
# Your implementation here
To compute the fitness of the organism, you can use organism.genes which is a list of floats [0, 1] that contains the value of each gene in the organism's genome.
Once your subclass has been implemented, you can run the algorithm using the simulate() method:
def simulate(self, genCount=0, minScore=sys.maxint, verbose=False):
"""Sets the genetic algorithm in motion
Simulates the genetic algorithm until:
1) genCount generations have elapsed
2) An organism has a fitness >= minScore
Arguments:
genCount (int): The number of generations to simulate. >= 0
minScore (float): The minimum score that qualifies as a solution.
verbose (bool, optional): Whether or not to print the current
generation and the best score of that generation for each
iteration
Returns:
Organism: The organism with the best score at the end of the
simulation
"""
Simply call this function from your subclass:
myAlg = GenAlgSubclass()
winningOrganism = myAlg.simulate()
Developed by William Ganucheau. Released under the MIT License.