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import numpy as np
import matplotlib.pyplot as plt
DO_VIS = True
# in-place
def clip_params(population, clipper_function):
for i, individual in enumerate(population):
population[i] = clipper_function(individual)
def evolution_strategies_optimizer(objective_function, clipper_function, init_mean, init_scale):
# Initialize parameters
population_size = 100
number_of_generations = 30
mutation_scale = 0.05
selection_ratio = 0.5
selected_size = int(population_size * selection_ratio)
# Initialize population (randomly)
population = np.random.normal(loc=init_mean, scale=init_scale, size=(population_size, len(init_mean)))
clip_params(population, clipper_function)
for generation in range(number_of_generations):
# Evaluate fitness
fitness = np.array([objective_function(individual) for individual in population])
# Select the best individuals
selected_indices = np.argsort(fitness)[:selected_size]
selected = population[selected_indices]
# Reproduce (mutate)
offspring = selected + np.random.normal(loc=0, scale=init_scale * mutation_scale, size=(selected_size, len(init_mean)))
clip_params(offspring, clipper_function) # in-place
# Replacement: Here we simply generate new candidates around the selected ones
population[:selected_size] = selected
population[selected_size:] = offspring
# Logging
best_fitness = fitness[selected_indices[0]]
best_index = np.argmin(fitness)
best_solution = population[best_index]
print(f"Generation {generation + 1}: Best Fitness = {best_fitness}", f"Best solution so far: {best_solution}")
if DO_VIS:
plt.scatter(population[:, 0], population[:, 1])
plt.xlim(-5, 5)
plt.ylim(-5, 5)
x = np.linspace(-5, 5, 100)
y = np.linspace(-5, 5, 100)
XY = np.stack(np.meshgrid(x, y), axis=-1)
print(XY.shape)
# Defining the function (x - 3)**2 + (y + 2)**2
Z = toy_objective_function(XY)
contour = plt.contour(XY[..., 0], XY[..., 1], Z, levels=50)
plt.show()
# Best solution
best_index = np.argmin(fitness)
best_solution = population[best_index]
print(f"Best solution found: {best_solution}")
return best_solution
def toy_objective_function(x):
return (x[..., 0] - 3)**2 + (x[..., 1] + 2)**2
def toy_clipper_function(x):
return x
def main():
init_mean = np.array([0.0, 0.0])
init_scale = np.array([10.0, 10.0])
best_solution = evolution_strategies_optimizer(toy_objective_function, toy_clipper_function, init_mean, init_scale)
if __name__ == '__main__':
main()
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