NIA

Instalation

Check NIA's PyPI page or simply install it using pip:

pip install nia

Usage

Solve Ackley problem using Genetic Algorithm:

from nia.algorithms import GeneticAlgorithm
from nia.problems import ackley


nia = GeneticAlgorithm(cost_function=ackley,
                       lower_bond=[-5,-5],
                       upper_bond=[5,5],
                                )
nia.run()
print(nia.message);

output:

quit criteria reached best answer is: [-0.02618036 -0.03615453] and best fitness is: 0.0006327163637145361 iteration : 11

Plot:

Customization:

from nia.algorithms import GeneticAlgorithm
# Specific selection, crossover and muttion algorithms are available under related sub-packages.
from nia.selections import Tournament
from nia.crossovers import RandomSBX
from nia.mutations import Uniform
import numpy as np

def ackley(X):
    x = X[0]
    y = X[1]
    return -20 * np.exp(-0.2 * np.sqrt(0.5 * (x**2 + y**2))) - np.exp(0.5 *
        (np.cos(2 * np.pi * x) + np.cos(2 * np.pi * y))) + np.e + 20

def log(ga):
  print(ga.best)

lower = np.array([-5,-5])
upper = np.array([5,5])

nia = GeneticAlgorithm(cost_function=ackley,
                       iteration_function=log,
                       lower_bond=lower,
                       upper_bond=upper,
                       quit_criteria = 0.0001,
                       num_variable = 2,
                       num_population = 20,
                       max_iteration = 100,
                       crossover = RandomSBX(2),
                       mutation = Uniform(0.05),
                       selection = Tournament(20)
                                )
nia.run()
print(nia.message);

output

max iteration reached best answer so far: [-0.02618036 -0.03615453] with best fitness: 0.1786046633597529 iteration : 99

Supported Algorithms :

• [x] Genetic algorithm (GeneticAlgorithm)
• [ ] Differential Evolution
• [ ] Evolutionary Programming
• [ ] Artificial Immune System
• [ ] Clonal Selection Algorithm
• [ ] Biogeography-based
• [ ] Symbiotic Organisms Search
• [ ] Ant Colony Optimization
• [x] Artificial Bee Colony (ArtificialBeeColony)
• [ ] Moth Flame Optimization Algorithm
• [ ] Cuckoo Search
• [ ] Green Herons Optimization Algorithm
• [ ] Bat Algorithm
• [ ] Whale Optimization Algorithm
• [ ] Krill Herd
• [ ] Fish-swarm Algorithm
• [ ] Grey Wolf Optimizer
• [ ] Shuffle frog-leaping Algorithm
• [ ] Cat Swarm Optimization
• [ ] Flower Pollination Algorithm
• [ ] Invasive Weed Optimization
• [ ] Water Cycle Algorithm
• [ ] Teaching–Learning-Based Optimization
• [x] Particle Swarm Optimization (ParticleSwarmOptimization)
• [ ] Simulated Annealing Algorithm
• [ ] Gravitational Search Algorithm
• [ ] Big Bang - Big Crunch

Supported Selection Operators :

• [x] Rank (Rank)
• [x] Tournament (Tournament)

Supported Cross Over Operators :

• [x] K-Point (KPoint)
• [x] SBX (SBX)
• [x] Random SBX (RandomSBX)

Supported Mutation Operators :

• [x] Uniform (Uniform)