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, 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.

Current Features:

Usage:

Example:

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

Contributing