# OptivolutionPy
A flexible genetic algorithm library written purly in Python3.
## Installation
For python3, simply run:
```sh
$ pip3 install OptivolutionPy
```
Or clone this repository and run python3 setup.py install from within the project directory. e.g.:
```sh
$ git clone https://github.com/Mhmd-Hisham/OptivolutionPy.git
$ cd OptivolutionPy
$ python3 setup.py install
```
## Advanced Example
#### Smart Ants using OptivolutionPy & Processing3. check this [repo](https://github.com/Mhmd-Hisham/SmartAntsGA) for more details.
## Simple example
### Solving the one-dimensional [knapsack problem](http://en.wikipedia.org/wiki/Knapsack_problem):
```python3
#!/usr/bin/env python3
import random
from optivolution.population import Population
from optivolution.chromosome import Chromosome
class OneDimensinalKnapsack(Chromosome):
""" Inidividual knapsack object. """
maximum_weight = 15
knapsack_data = [{'name': 'box1', 'value': 4, 'weight': 12},
{'name': 'box2', 'value': 2, 'weight': 1},
{'name': 'box3', 'value': 10, 'weight': 4},
{'name': 'box4', 'value': 1, 'weight': 1},
{'name': 'box5', 'value': 2, 'weight': 2}]
def __init__(self, genes_length=len(knapsack_data), genes=[]):
super().__init__(genes_length, genes)
@Chromosome.fitness_property
def fitness(self):
""" Defining the fitness function. """
# Use the knapsack value as the fitness value
total_value = 0
total_weight = 0
for i in range(self.genes_length):
if (self.genes[i] == True):
total_value += self.knapsack_data[i]['value']
total_weight += self.knapsack_data[i]['weight']
if total_weight > self.maximum_weight:
total_value = 0
return total_value
def random_gene(self):
""" Defining the random gene. """
return random.choice((0, 1))
class KnapscakPopulation(Population):
tournament_sample_percentage = 10
def random_individual(self):
""" Defining the random individual in the population. """
return OneDimensinalKnapsack()
def main():
population = KnapscakPopulation(population_size=20)
population.run(20)
print(f"Generation {population.generation_number}")
best = population.get_best_individual()
# The optimal answer for this test case is
# (15, [0, 1, 1, 1, 1])
print((best.fitness, best.genes))
if __name__ == "__main__":
main()
```
Output:
```
(15, [0, 1, 1, 1, 1])
```
## Meta
Mohamed Hisham – [G-Mail](mailto:Mohamed00Hisham@Gmail.com) | [GitHub](https://github.com/Mhmd-Hisham) | [LinkedIn](https://www.linkedin.com/in/Mhmd-Hisham/)
This project is licensed under the GNU GPLv3 License - check [LICENSE](https://github.com/Mhmd-Hisham/OptivolutionPy/blob/master/LICENSE) for more details.
