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Nonlinear Programming

Binary Genetic Algorithm (BGA)

from sigmaepsilon.math.optimize import BinaryGeneticAlgorithm


def Rosenbrock(x):
    a, b = 1, 100
    return (a-x[0])**2 + b*(x[1]-x[0]**2)**2


ranges = [[-10, 10], [-10, 10]]
BGA = BinaryGeneticAlgorithm(Rosenbrock, ranges, length=12, nPop=200)
BGA.solve()

Genom(phenotype=[0.6080586080586077, 0.19291819291819223], genotype=[0, 0, 1, 1, 1, 1, 1, 0, 0, 0, 0, 1, 1, 1, 1, 0, 0, 1, 0, 0, 0, 0, 0, 1], fittness=3.2800459589915025, age=100)

Iterative Solution using BGA.evolve

Here we keep track of the best candidate and plot a history chart using matplotlib. The evolve call on the object performs a specified number of cycles, while best_phenotype returns the best candidate in general format.

import matplotlib.pyplot as plt

BGA = BinaryGeneticAlgorithm(Rosenbrock, ranges, length=12, nPop=200)
history = [Rosenbrock(BGA.best_phenotype())]
for _ in range(100):
    BGA.evolve(1)
    history.append(Rosenbrock(BGA.best_phenotype()))
plt.plot(history)
plt.show()
x = BGA.best_phenotype()
fx = Rosenbrock(x)