Python實現遺傳算法庫

scikit-opt

scikit-opt 這個庫以很好用的方式實現了遺傳算法的應用，是目前能找到的較好的遺傳算法工具箱

目標函數

def demo_func(x):
    x1, x2, x3 = x
    return x1 ** 2 + (x2 - 0.05) ** 2 + x3 ** 2

用遺傳算法求解

from ga import GA
ga = GA(func=demo_func, lb=[-1, -10, -5], ub=[2, 10, 2], max_iter=500)
best_x, best_y = ga.fit()

還提供一個每代數據的藉口，可以拿來畫各個維度的圖

import pandas as pd
import matplotlib.pyplot as plt
FitV_history = pd.DataFrame(ga.FitV_history)
fig, ax = plt.subplots(2, 1)
ax[0].plot(FitV_history.index, FitV_history.values, '.', color='red')
plt_max = FitV_history.max(axis=1)
ax[1].plot(plt_max.index, plt_max, label='max')
ax[1].plot(plt_max.index, plt_max.cummax())
plt.show()

2. 用遺傳算法解決TSP問題(Travelling Salesman Problem)

加載距離矩陣的數據，這裏用隨機數生成數據

from GA import GA_TSP
import numpy as np

num_points = 8

points = range(num_points)
points_coordinate = np.random.rand(num_points, 2)
distance_matrix = np.zeros(shape=(num_points, num_points))
for i in range(num_points):
    for j in range(num_points):
        distance_matrix[i][j] = np.linalg.norm(points_coordinate[i] - points_coordinate[j], ord=2)
print('distance_matrix is: \n', distance_matrix)


def cal_total_distance(points):
    num_points, = points.shape
    total_distance = 0
    for i in range(num_points - 1):
        total_distance += distance_matrix[points[i], points[i + 1]]
    total_distance += distance_matrix[points[i + 1], points[0]]
    return total_distance

用遺傳算法求解：

ga_tsp = GA_TSP(func=cal_total_distance, points=points, pop=50, max_iter=200, Pm=0.001)
best_points, best_distance = ga_tsp.fit()

畫圖：

fig, ax = plt.subplots(1, 1)
best_points_ = np.concatenate([best_points, [best_points[0]]])
best_points_coordinate = points_coordinate[best_points_, :]
ax.plot(best_points_coordinate[:, 0], best_points_coordinate[:, 1],'o-r')
plt.show()