diff --git a/CHANGELOG.md b/CHANGELOG.md
index 9613e291..c4b8bb23 100644
--- a/CHANGELOG.md
+++ b/CHANGELOG.md
@@ -4,7 +4,7 @@ All notable changes to this project will be documented in this file.
 
 The format is based on [Keep a Changelog](http://keepachangelog.com/) and this project adheres to [Semantic Versioning](http://semver.org/).
 
-## [Unreleased]
+## [0.1.0] -2022-04-14
 
 ### Added
 
diff --git a/README(JP).md b/README(JP).md
deleted file mode 100644
index e42de507..00000000
--- a/README(JP).md
+++ /dev/null
@@ -1,12 +0,0 @@
-# Tunny
-
-**Tunny** はオープンソースのハイパーパラメータ自動最適化フレームワーク Optuna を使った Grasshopper の最適化コンポーネントです。
-以下は公式サイトより引用
-
-> オープンソースのハイパーパラメータ自動最適化フレームワーク Optuna™ は、ハイパーパラメータの値に関する試行錯誤を自動化し、優れた性能を発揮するハイパーパラメータの値を自動的に発見します。オープンソースの深層学習フレームワーク Chainer をはじめ、様々な機械学習ソフトウェアと一緒に使用することが可能です。
-> 
-> Optuna は、物体検出コンペティション [Google AI Open Images 2018 – Object Detection Track](https://www.preferred.jp/ja/news/pr20180907/) など、PFN の各プロジェクトで活用され、成果をあげています。
-
-
-Optuna の公式サイト
-- https://optuna.org/
diff --git a/README.md b/README.md
index 6082ff94..733c18c8 100644
--- a/README.md
+++ b/README.md
@@ -72,7 +72,7 @@ Input of large size meshes is deprecated because it makes the analysis heavier.
 
 ![image](https://user-images.githubusercontent.com/23289252/163379419-40368cc4-8abd-40d0-94ca-d0a468796c57.png)
 
-### Optimize Window
+### Optimization Window
 
 Double-click on the component icon to open the form for performing optimization.
 
@@ -89,9 +89,10 @@ Values that can be set and their meanings are as follows.
 
 - Sampler
   - Sets the algorithm to perform the optimization. The following types are available.
-    1. TPE (Tree-structured Parzen Estimator)
-    1. NSGA-II
-    1. CMA-ES
+  - All are optimization algorithms provided by Optuna.
+    1. TPE (Bayesian optimization)
+    1. NSGA-II (Genetic algorithm)
+    1. CMA-ES (Evolution strategy)
     1. Random
 - Number of trial
   - This number of trials will be performed.
@@ -122,6 +123,7 @@ Values that can be set and their meanings are as follows.
   - Open the folder where the file containing the optimization results is located. The results are stored under the name "Tunny_Opt_Result.db".
 - Clear result file
   - Deletes the optimization result file.
+  - If the value of the input changes, delete it if necessary, since optimization using a file containing the same name study will cause content conflicts and optimization will not be performed.
 - Set restore model number
   - The model with the number entered here is restored from the optimization results file and is the output of the component.
   - The model number matches the tree structure of the output.
@@ -147,7 +149,7 @@ Or [pixivFANBOX](https://hiron.fanbox.cc/)
 ## License
 
 Tunny is licensed under the [MIT](https://github.com/hrntsm/Tunny/blob/main/LICENSE) license.  
-Copyright© 2019-2021, hrntsm
+Copyright© 2022, hrntsm
 
 Release package is embedded Python runtime & optuna libraries.
 These depend on their own licenses.