diff --git a/.gitignore b/.gitignore
index bd1349c..c3e944e 100644
--- a/.gitignore
+++ b/.gitignore
@@ -28,6 +28,7 @@ share/python-wheels/
 MANIFEST
 **/_plots/
 **/_data/
+**/_gifs/
 
 # Dev
 *___.*
diff --git a/docs/plot_search_path/create_search_path_gifs.py b/docs/plot_search_path/create_search_path_gifs.py
deleted file mode 100644
index f2acd9a..0000000
--- a/docs/plot_search_path/create_search_path_gifs.py
+++ /dev/null
@@ -1,31 +0,0 @@
-import imageio
-
-
-optimizer_keys = ["HillClimbingOptimizer"]
-n_iter_list = range(1, 51)
-
-
-def get_path(optimizer_key, nth_iteration):
-    return (
-        "./_plots/"
-        + str(optimizer_key)
-        + "_"
-        + "{0:0=2d}".format(nth_iteration)
-        + ".jpg"
-    )
-
-
-filepaths = [
-    get_path(opt, nth_iter)
-    for opt in optimizer_keys
-    for nth_iter in n_iter_list
-]
-
-
-images = []
-for filename in filepaths:
-    images.append(imageio.imread(filename))
-
-print("Save gif...")
-imageio.mimsave("movie.gif", images, duration=0.3)
-
diff --git a/docs/search_path_gifs/create_search_path_gifs.py b/docs/search_path_gifs/create_search_path_gifs.py
new file mode 100644
index 0000000..b9da66f
--- /dev/null
+++ b/docs/search_path_gifs/create_search_path_gifs.py
@@ -0,0 +1,191 @@
+import os
+import glob
+
+import numpy as np
+import pandas as pd
+from tqdm import tqdm
+import matplotlib.pyplot as plt
+
+from gradient_free_optimizers.converter import Converter
+
+
+def plot_search_paths(
+    opt_name,
+    optimizer,
+    opt_para,
+    n_iter_list,
+    objective_function,
+    objective_function_np,
+    search_space,
+    initialize,
+    random_state,
+):
+    for n_iter in tqdm(n_iter_list):
+        opt = optimizer(search_space, **opt_para)
+
+        opt.search(
+            objective_function,
+            n_iter=n_iter,
+            random_state=random_state,
+            memory=False,
+            verbosity=False,
+            initialize=initialize,
+        )
+
+        conv = Converter(search_space)
+
+        plt.figure(figsize=(10, 8))
+        plt.set_cmap("jet_r")
+
+        x_all, y_all = search_space["x"], search_space["y"]
+        xi, yi = np.meshgrid(x_all, y_all)
+        zi = objective_function_np(xi, yi)
+
+        plt.imshow(
+            zi,
+            alpha=0.15,
+            # vmin=z.min(),
+            # vmax=z.max(),
+            # origin="lower",
+            extent=[x_all.min(), x_all.max(), y_all.min(), y_all.max()],
+        )
+
+        # print("\n Results \n", opt.results)
+
+        for n, opt_ in enumerate(opt.optimizers):
+            pos_list = np.array(opt_.pos_new_list)
+            score_list = np.array(opt_.score_new_list)
+
+            values_list = conv.positions2values(pos_list)
+            values_list = np.array(values_list)
+
+            plt.plot(
+                values_list[:, 0],
+                values_list[:, 1],
+                linestyle="--",
+                marker=",",
+                color="black",
+                alpha=0.33,
+                label=n,
+                linewidth=0.5,
+            )
+            plt.scatter(
+                values_list[:, 0],
+                values_list[:, 1],
+                c=score_list,
+                marker="H",
+                s=15,
+                vmin=-20000,
+                vmax=0,
+                label=n,
+                edgecolors="black",
+                linewidth=0.3,
+            )
+
+        plt.xlabel("x")
+        plt.ylabel("y")
+
+        nth_iteration = "\n\nnth Iteration: " + str(n_iter)
+
+        plt.title(opt_name + nth_iteration)
+
+        plt.xlim((-101, 101))
+        plt.ylim((-101, 101))
+        plt.colorbar()
+        # plt.legend(loc="upper left", bbox_to_anchor=(-0.10, 1.2))
+
+        plt.tight_layout()
+        plt.savefig(
+            "./_plots/"
+            + str(opt.__class__.__name__)
+            + "_"
+            + "{0:0=3d}".format(n_iter)
+            + ".jpg",
+            dpi=300,
+        )
+        plt.close()
+
+
+def create_search_path_gif(
+    opt_name,
+    optimizer,
+    opt_para,
+    n_iter,
+    objective_function,
+    objective_function_np,
+    search_space,
+):
+    pass
+
+
+########################################################################
+
+
+from gradient_free_optimizers import HillClimbingOptimizer
+
+optimizer_keys = ["HillClimbingOptimizer"]
+n_iter_list = range(1, 51)
+
+
+def get_path(optimizer_key, nth_iteration):
+    return (
+        "./_plots/"
+        + str(optimizer_key)
+        + "_"
+        + "{0:0=2d}".format(nth_iteration)
+        + ".jpg"
+    )
+
+
+def objective_function(pos_new):
+    score = -(pos_new["x"] * pos_new["x"] + pos_new["y"] * pos_new["y"])
+    return score
+
+
+def objective_function_np(x1, x2):
+    score = -(x1 * x1 + x2 * x2)
+    return score
+
+
+search_space = {"x": np.arange(-100, 101, 1), "y": np.arange(-100, 101, 1)}
+
+n_iter_list = range(1, 3)
+opt_para = {}
+initialize = {"vertices": 2}
+random_state = 0
+
+para_dict = (
+    "hill_climbing.gif",
+    {
+        "opt_name": "Hill climbing",
+        "optimizer": HillClimbingOptimizer,
+        "opt_para": opt_para,
+        "n_iter_list": n_iter_list,
+        "objective_function": objective_function,
+        "objective_function_np": objective_function_np,
+        "search_space": search_space,
+        "initialize": initialize,
+        "random_state": random_state,
+    },
+)
+
+
+for _para_dict in tqdm([para_dict]):
+    plot_search_paths(**_para_dict[1])
+
+    _framerate = " -framerate 3 "
+    _input = " -i ./_plots/HillClimbingOptimizer_%03d.jpg "
+    _scale = " -vf scale=1200:-1 "
+    _output = " ./_gifs/" + _para_dict[0]
+
+    ffmpeg_command = "ffmpeg -y" + _framerate + _input + _scale + _output
+    print("\n\n -----> ffmpeg_command \n", ffmpeg_command, "\n\n")
+    print(_para_dict[0])
+
+    os.system(ffmpeg_command)
+
+    rm_files = glob.glob("./_plots/*.jpg")
+
+    for f in rm_files:
+        os.remove(f)
+