Update README.md

README.md

@@ -7,7 +7,7 @@ Lightweight Covariance Matrix Adaptation Evolution Strategy (CMA-ES) [1] impleme
 ![visualize-six-hump-camel](https://user-images.githubusercontent.com/5564044/73486622-db5cff00-43e8-11ea-98fb-8246dbacab6d.gif)
 
 ## News
-* **2023/08/07** [LRA-CMA-ES (CMA-ES with Learning Rate Adaptation)](https://arxiv.org/abs/2304.03473) has been incorporated into [Optuna](https://optuna.org/), a highly popular software for hyperparameter optimization! You can use it through [CmaEsSampler](https://optuna.readthedocs.io/en/stable/reference/samplers/generated/optuna.samplers.CmaEsSampler.html) by simply setting the argument `lr_adapt=True`☕
+* **2023/08/07** [LRA-CMA-ES (CMA-ES with Learning Rate Adaptation)](https://arxiv.org/abs/2304.03473) has been incorporated into [Optuna](https://optuna.org/), a highly popular software for hyperparameter optimization! You can use it through [CmaEsSampler](https://optuna.readthedocs.io/en/stable/reference/samplers/generated/optuna.samplers.CmaEsSampler.html) by simply setting the argument `lr_adapt=True`. Note that you can also use [Warm Starting CMA-ES](https://arxiv.org/abs/2012.06932) and [CMA-ES with Margin](https://arxiv.org/abs/2205.13482) from [CmaEsSampler in Optuna](https://optuna.readthedocs.io/en/stable/reference/samplers/generated/optuna.samplers.CmaEsSampler.html).
 * **2023/05/23** Our paper, [M. Nomura, Y. Akimoto, and I. Ono, CMA-ES with Learning Rate Adaptation: Can CMA-ES with Default Population Size Solve Multimodal and Noisy Problems?](https://arxiv.org/abs/2304.03473), has been nominated for the Best Paper Award in the ENUM track at GECCO'23 :whale:
 * **2023/04/01** Two papers have been accepted to GECCO'23 ENUM Track: (1) [M. Nomura, Y. Akimoto, and I. Ono, CMA-ES with Learning Rate Adaptation: Can CMA-ES with Default Population Size Solve Multimodal and Noisy Problems?](https://arxiv.org/abs/2304.03473), and (2) [Y. Watanabe, K. Uchida, R. Hamano, S. Saito, M. Nomura, and S. Shirakawa, (1+1)-CMA-ES with Margin for Discrete and Mixed-Integer Problems](https://arxiv.org/abs/2305.00849) :tada:
 * **2022/05/13** The paper, ["CMA-ES with Margin: Lower-Bounding Marginal Probability for Mixed-Integer Black-Box Optimization"](https://arxiv.org/abs/2205.13482) written by Hamano, Saito, [@nomuramasahir0](https://github.com/nomuramasahir0) (the maintainer of this library), and Shirakawa, has been nominated as best paper at GECCO'22 ENUM track.