遗传算法

GA_Agent_0925/绘图.py

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+import pandas as pd
+import matplotlib.pyplot as plt
+
+# # ==============================
+# # 1️⃣ 读取 CSV
+# # ==============================
+# csv_file = "convergence0119_data.csv"
+# df = pd.read_csv(csv_file)
+#
+# # ==============================
+# # 2️⃣ 选择一次完整 GA 运行
+# # ==============================
+# target_ts = "2026-01-26 22:30:22"
+# run_df = df[df["Timestamp"] == target_ts].copy()
+#
+# # 按代数排序，取前 50 代
+# run_df = run_df.sort_values("Generation").head(100)
+
+# 读取 CSV
+csv_file = "convergence0119_data.csv"
+df = pd.read_csv(csv_file)
+
+# 按 Generation 排序
+run_df = df.sort_values("Generation")
+# ==============================
+# 3️⃣ 提取数据
+# ==============================
+generations = run_df["Generation"].values
+best_fitness = run_df["Best_Fitness_Percentage"].values
+
+# ==============================
+# 4️⃣ 计算滑动平均（窗口=5，可调）
+# ==============================
+window_size = 5
+run_df["MA"] = run_df["Best_Fitness_Percentage"].rolling(
+    window=window_size,
+    min_periods=1
+).mean()
+
+# ==============================
+# 5️⃣ 绘图：点 + 连线 + 滑动平均
+# ==============================
+plt.figure(figsize=(10, 6))
+
+# 原始点
+plt.scatter(generations, best_fitness, s=30, alpha=0.6, label="Best Error")
+
+# 连线
+plt.plot(generations, best_fitness, linewidth=1, alpha=0.6)
+
+plt.xlabel("Generation")
+plt.ylabel("Best Error")
+plt.legend()
+plt.grid(True, alpha=0.3)
+plt.tight_layout()
+
+plt.savefig("ga_convergence_me_.png", dpi=300)
+plt.show()