add description of our library's characteristics

README.md

@@ -1,9 +1,19 @@
-# CMA-ES
+# CyberAgentAILab/cmaes
 
 [![Software License](https://img.shields.io/badge/license-MIT-brightgreen.svg?style=flat-square)](./LICENSE) [![PyPI - Downloads](https://img.shields.io/pypi/dw/cmaes)](https://pypistats.org/packages/cmaes)
 
-Simple and Practical CMA-ES [Hansen 2016] implementation in Python.
+**_Simple_** and **_Practical_** CMA-ES implementation in Python.
 
+#### _Simplicity_ :
+* [CMA-ES](https://arxiv.org/abs/1604.00772) [Hansen 2016] implementation with *high code readability*
+* This is suitable for quick use of CMA-ES as well as for educational purposes and easy integration into other
+libraries
+
+#### _Practicality_ :
+* implementations of highly practical methods with easy-to-use APIs
+* The methods include [LRA-CMA](https://arxiv.org/abs/2304.03473) [Nomura et al. 2023] (removing hyperparameter tuning), [WS-CMA](https://arxiv.org/abs/2012.06932) [Nomura et al. 2021] (transfer learning), and [CMAwM](https://arxiv.org/abs/2205.13482) [Hamano et al. 2022] (mixed-integer optimization)
+
+(We also provide [animated visualization](https://github.com/CyberAgentAILab/cmaes/blob/main/tools/cmaes_visualizer.py).)
 ![visualize-six-hump-camel](https://user-images.githubusercontent.com/5564044/73486622-db5cff00-43e8-11ea-98fb-8246dbacab6d.gif)
 
 ## Installation
@@ -341,16 +351,16 @@ Full source code is available [here](./examples/ipop_cma.py).
 
 * [Optuna](https://github.com/optuna/optuna) : A hyperparameter optimization framework that supports CMA-ES using this library under the hood.
 * [Kubeflow/Katib](https://www.kubeflow.org/docs/components/katib/katib-config/) : Kubernetes-based system for hyperparameter tuning and neural architecture search
-* (If you have a project which uses `cmaes` and want your own project to be listed here, please submit a GitHub issue.)
+* (If you are using `cmaes` in your project and would like it to be listed here, please submit a GitHub issue.)
 
 **Other libraries:**
 
-I respect all libraries involved in CMA-ES.
+We have great respect for all libraries involved in CMA-ES.
 
-* [pycma](https://github.com/CMA-ES/pycma) : Most famous CMA-ES implementation by Nikolaus Hansen.
-* [pymoo](https://github.com/msu-coinlab/pymoo) : Multi-objective optimization in Python.
-* [evojax](https://github.com/google/evojax) : EvoJAX provides a JAX-port of this library.
-* [evosax](https://github.com/RobertTLange/evosax) : evosax provides JAX-based CMA-ES and sep-CMA-ES implementation, which is inspired by this library.
+* [pycma](https://github.com/CMA-ES/pycma) : Most renowned CMA-ES implementation, created and maintained by Nikolaus Hansen.
+* [pymoo](https://github.com/msu-coinlab/pymoo) : A library for multi-objective optimization in Python.
+* [evojax](https://github.com/google/evojax) : EvoJAX offers a JAX-port of this library.
+* [evosax](https://github.com/RobertTLange/evosax) : evosax provides a JAX-based implementation of CMA-ES and sep-CMA-ES, inspired by this library.
 
 **References:**