Skip to main content

A python metaheuristic optimization library. Currently supports Genetic Algorithms, Gravitational Search, and Cross Entropy.

Project description

Optimal (beta)

A python metaheuristic optimization library. Built for easy extension and usage.

Warning: Optimal is in beta. API may change. I will do my best to note any breaking changes in this readme, but no guarantee is given.

Supported metaheuristics:

  • Genetic algorithms (GA)

  • Gravitational search algorithm (GSA)

  • Cross entropy (CE)


pip install optimal


import math

from optimal import GenAlg
from optimal import Problem
from optimal import helpers

# The genetic algorithm uses binary solutions.
# A decode function is useful for converting the binary solution to real numbers
def decode_ackley(binary):
    # Helpful functions from helpers are used to convert binary to float
    # x1 and x2 range from -5.0 to 5.0
    x1 = helpers.binary_to_float(binary[0:16], -5.0, 5.0)
    x2 = helpers.binary_to_float(binary[16:32], -5.0, 5.0)
    return x1, x2

# ackley is our fitness function
# This is how a user defines the goal of their problem
def ackley_fitness(solution):
    x1, x2 = solution

    # Ackley's function
    # A common mathematical optimization problem
    output = -20 * math.exp(-0.2 * math.sqrt(0.5 * (x1**2 + x2**2))) - math.exp(
        0.5 * (math.cos(2 * math.pi * x1) + math.cos(2 * math.pi * x2))) + 20 + math.e

    # You can prematurely stop the metaheuristic by returning True
    # as the second return value
    # Here, we consider the problem solved if the output is <= 0.01
    finished = output <= 0.01

    # Because this function is trying to minimize the output,
    # a smaller output has a greater fitness
    fitness = 1 / output

    # First return argument must be a real number
    # The higher the number, the better the solution
    # Second return argument is a boolean, and optional
    return fitness, finished

# Define a problem instance to optimize
# We can optionally include a decode function
# The optimizer will pass the decoded solution into your fitness function
# Additional fitness function and decode function parameters can also be added
ackley = Problem(ackley_fitness, decode_function=decode_ackley)

# Create a genetic algorithm with a chromosome size of 32,
# and use it to solve our problem
my_genalg = GenAlg(32)
best_solution = my_genalg.optimize(ackley)

print best_solution

Important notes:

  • Fitness function must take solution as its first argument

  • Fitness function must return a real number as its first return value

For further usage details, see comprehensive doc strings.

Major Changes


Moved a number of options from Optimizer to Optimizer.optimize


Renamed common.random_solution_binary to common.random_binary_solution, and common.random_solution_real to common.random_real_solution


problem now an argument of Optimizer.optimize, instead of Optimizer.__init__.


max_iterations now an argument of Optimizer.optimize, instead of Optimizer.__init__.


Optimizer now takes a problem instance, instead of a fitness function and kwargs.


Library reorganized with greater reliance on

Optimizers can now be imported with:

from optimal import GenAlg, GSA, CrossEntropy


Project details

Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution (49.1 kB view hashes)

Uploaded source

Supported by

AWS AWS Cloud computing Datadog Datadog Monitoring Facebook / Instagram Facebook / Instagram PSF Sponsor Fastly Fastly CDN Google Google Object Storage and Download Analytics Huawei Huawei PSF Sponsor Microsoft Microsoft PSF Sponsor NVIDIA NVIDIA PSF Sponsor Pingdom Pingdom Monitoring Salesforce Salesforce PSF Sponsor Sentry Sentry Error logging StatusPage StatusPage Status page