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Author: Evolutionary-Intelligence

tutorials/plot_2d_evolution_for_maes.py

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+"""该绘图程序被段琦琦的博士论文（哈工大与南科大联合培养）所使用。
+"""
+import numpy as np
+import matplotlib
+import matplotlib.pyplot as plt
+
+from pypop7.benchmarks.utils import generate_xyz
+# abstract class for all evolution Strategies
+from pypop7.optimizers.es.es import ES
+# Matrix Adaptation Evolution Strategy
+from pypop7.optimizers.es.maes import MAES
+
+
+matplotlib.rcParams['font.family'] = 'sans-serif'
+matplotlib.rcParams['font.sans-serif'] = 'SimSun'
+# matplotlib.rcParams['font.size'] = 10  # 
+
+
+def cd(x):  # from https://arxiv.org/pdf/1610.00040v1.pdf
+    return 7.0 * (x[0] ** 2) + 6.0 * x[0] * x[1] + 8.0 * (x[1] ** 2)
+
+
+# helper function for 2D-plotting
+def plot_contour(func, x, y):
+    x, y, z = generate_xyz(func, x, y, 200)
+    plt.contourf(x, y, z, cmap='bone')
+    plt.contour(x, y, z, colors='white', alpha=0.5)
+
+
+def plot(xs, means, bound=[-10.0, 10.0]):
+    for i in range(len(xs)):
+        plt.figure(figsize=(2.5, 2.5))
+        plt.title('不可分函数', fontsize=10)
+        plt.xlim(bound)
+        plt.ylim(bound)
+        plt.xticks(fontsize=10)
+        plt.yticks(fontsize=10)
+        plot_contour(cd, bound, bound)
+        plt.scatter(xs[i][:, 0], xs[i][:, 1], color='green')
+        plt.scatter(means[i][0], means[i][1], color='red')
+        plt.xlabel('维度1', fontsize=10)
+        plt.ylabel('维度2', fontsize=10, labelpad=-1)
+        plt.savefig(str(i) + '.png', dpi=700, bbox_inches='tight')
+        plt.show()
+
+
+class PlotMaes(MAES):
+    def optimize(self, fitness_function=None, args=None):
+        fitness = ES.optimize(self, fitness_function)
+        z, d, mean, s, tm, y = self.initialize()
+        xs, means = [], []  # only for plotting
+        while not self._check_terminations():
+            z, d, y = self.iterate(z, d, mean, tm, y, args)
+            if self.saving_fitness and (not self._n_generations % self.saving_fitness):
+                xs.append(self.sigma * d + mean)  # only for plotting
+                means.append(mean.copy())  # only for plotting
+            mean, s, tm = self._update_distribution(z, d, mean, s, tm, y)
+            self._print_verbose_info(fitness, y)
+            self._n_generations += 1
+        res = self._collect(fitness, y, mean)
+        return res, xs, means
+
+
+if __name__ == '__main__':
+    ndim_problem = 2
+    problem = {'fitness_function': cd,
+               'ndim_problem': ndim_problem,
+               'lower_boundary': -10.0 * np.ones((ndim_problem,)),
+               'upper_boundary': 10.0 * np.ones((ndim_problem,))}
+    options = {'max_function_evaluations': 3e3,
+               'n_individuals': 200,
+               'seed_rng': 2022,
+               'x': (7.0, -7.0),
+               'sigma': 0.05,
+               # to record best-so-far fitness every 50 function evaluations
+               'saving_fitness': 4,
+               'is_restart': False}
+    _, xs, means = PlotMaes(problem, options).optimize()
+    plot(xs, means)