https://github.com/100/Solid

🎯 A comprehensive gradient-free optimization framework written in Python
https://github.com/100/Solid

algorithm artificial-intelligence continuous-optimization discrete-optimization evolutionary-algorithm genetic-algorithm genetic-algorithm-framework harmony-search hill-climbing library machine-learning machine-learning-algorithms metaheuristics optimization optimization-algorithms particle-swarm-optimization python simulated-annealing stochastic-optimizers tabu-search

Last synced: 3 months ago
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🎯 A comprehensive gradient-free optimization framework written in Python

• Host: GitHub
• URL: https://github.com/100/Solid
• Owner: 100
• License: mit
• Created: 2017-06-12T05:02:08.000Z (over 7 years ago)
• Default Branch: master
• Last Pushed: 2019-07-19T15:31:43.000Z (over 5 years ago)
• Last Synced: 2024-07-31T17:44:11.374Z (3 months ago)
• Topics: algorithm, artificial-intelligence, continuous-optimization, discrete-optimization, evolutionary-algorithm, genetic-algorithm, genetic-algorithm-framework, harmony-search, hill-climbing, library, machine-learning, machine-learning-algorithms, metaheuristics, optimization, optimization-algorithms, particle-swarm-optimization, python, simulated-annealing, stochastic-optimizers, tabu-search
• Language: Python
• Homepage: https://100.github.io/Solid/
• Size: 268 KB
• Stars: 576
• Watchers: 12
• Forks: 64
• Open Issues: 7
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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[![Build Status](https://travis-ci.org/100/Solid.svg?branch=master)](https://travis-ci.org/100/Solid)

[![MIT License](https://img.shields.io/dub/l/vibe-d.svg)](https://github.com/100/Cranium/blob/master/LICENSE)

## *Solid* is a Python framework for gradient-free optimization.

#### It contains basic versions of many of the most common [optimization algorithms that do not require the calculation of gradients](https://en.wikipedia.org/wiki/Derivative-free_optimization), and allows for very rapid development using them.

#### It's a very versatile library that's great for learning, modifying, and of course, using out-of-the-box.

## See the detailed documentation [here](https://100.github.io/Solid/).



## Current Features:

* [Genetic Algorithm](https://github.com/100/Solid/blob/master/Solid/GeneticAlgorithm.py)

* [Evolutionary Algorithm](https://github.com/100/Solid/blob/master/Solid/EvolutionaryAlgorithm.py)

* [Simulated Annealing](https://github.com/100/Solid/blob/master/Solid/SimulatedAnnealing.py)

* [Particle Swarm Optimization](https://github.com/100/Solid/blob/master/Solid/ParticleSwarm.py)

* [Tabu Search](https://github.com/100/Solid/blob/master/Solid/TabuSearch.py)

* [Harmony Search](https://github.com/100/Solid/blob/master/Solid/HarmonySearch.py)

* [Stochastic Hill Climb](https://github.com/100/Solid/blob/master/Solid/StochasticHillClimb.py)



## Usage:

* ```pip install solidpy``` 

* Import the relevant algorithm

* Create a class that inherits from that algorithm, and that implements the necessary abstract methods

* Call its ```.run()``` method, which always returns the best solution and its objective function value



## Example:

```python

from random import choice, randint, random

from string import lowercase

from Solid.EvolutionaryAlgorithm import EvolutionaryAlgorithm

class Algorithm(EvolutionaryAlgorithm):

    """

    Tries to get a randomly-generated string to match string "clout"

    """

    def _initial_population(self):

        return list(''.join([choice(lowercase) for _ in range(5)]) for _ in range(50))

    def _fitness(self, member):

        return float(sum(member[i] == "clout"[i] for i in range(5)))

    def _crossover(self, parent1, parent2):

        partition = randint(0, len(self.population[0]) - 1)

        return parent1[0:partition] + parent2[partition:]

    def _mutate(self, member):

        if self.mutation_rate >= random():

            member = list(member)

            member[randint(0,4)] = choice(lowercase)

            member = ''.join(member)

        return member

def test_algorithm():

    algorithm = Algorithm(.5, .7, 500, max_fitness=None)

    best_solution, best_objective_value = algorithm.run()

```



## Testing

To run tests, look in the ```tests``` folder. 

Use [pytest](https://docs.pytest.org/en/latest/); it should automatically find the test files. 



## Contributing

Feel free to send a pull request if you want to add any features or if you find a bug.

Check the issues tab for some potential things to do.