import benchmark_functions as bf
import matplotlib.pyplot as plt
from ga import GA
from de import DE
# Select function for minimization
f = bf.Sphere(dim=2)
# View 3d plot of the function
if f.dim == 2:
bf.plot3d(function=f, show_contour=True)
plt.show()
# Create algorithm, Differential evolution or Genetic algorithm
de = DE(obj_function=f, F=0.8, Cr=0.3, gen_max=10000)
ga = GA(obj_function=f, k=70, pm=0.5, gen_max=10000, sigma=2)
# Find solution
de_solution, de_gen = de.search(show_progress=False, error_tolerance=f.epsilon)
ga_solution, ga_gen = ga.search(show_progress=False,
error_tolerance=f.epsilon,
c=0.9,
msc=50)
# Print results
print("============== Differential evolution ==============")
print(
"Solution {:f} found in {:d} generations. Optimal value is {:f}. Error is: {:f}."
.format(f.value(de_solution), de_gen, f.optimum,
f.value(de_solution) - f.optimum))
print("============== Genetic algorithm ==============")
print(
"Solution {:f} found in {:d} generations. Optimal value is {:f}. Error is: {:f}."
.format(f.value(ga_solution), ga_gen, f.optimum,
