Real-valued Optimization by Subspace Projection and Multi-armed Bandit Techniques
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There are many great README templates available on GitHub, however, I didn't find one that really suit my needs so I created this enhanced one. I want to create a README template so amazing that it'll be the last one you ever need -- I think this is it.
Here's why:
- Your time should be focused on creating something amazing. A project that solves a problem and helps others
- You shouldn't be doing the same tasks over and over like creating a README from scratch
- You should element DRY principles to the rest of your life 😄
Of course, no one template will serve all projects since your needs may be different. So I'll be adding more in the near future. You may also suggest changes by forking this repo and creating a pull request or opening an issue. Thanks to all the people have have contributed to expanding this template!
A list of commonly used resources that I find helpful are listed in the acknowledgements.
This section should list any major frameworks that you built your project using. Leave any add-ons/plugins for the acknowledgements section. Here are a few examples.
This is an example of how you may give instructions on setting up your project locally. To get a local copy up and running follow these simple example steps.
This is an example of how to list things you need to use the software and how to install them.
- npm
npm install npm@latest -g
- Get a free API Key at https://example.com
- Clone the repo
git clone https://github.com/your_username_/Project-Name.git
- Install NPM packages
npm install
- Enter your API in
config.js
const API_KEY = 'ENTER YOUR API';
Use this space to show useful examples of how a project can be used. Additional screenshots, code examples and demos work well in this space. You may also link to more resources.
For more examples, please refer to the Documentation
See the open issues for a list of proposed features (and known issues).
Contributions are what make the open source community such an amazing place to be learn, inspire, and create. Any contributions you make are greatly appreciated.
- Fork the Project
- Create your Feature Branch (
git checkout -b feature/AmazingFeature
) - Commit your Changes (
git commit -m 'Add some AmazingFeature'
) - Push to the Branch (
git push origin feature/AmazingFeature
) - Open a Pull Request
Distributed under the MIT License. See LICENSE
for more information.
If you use this research/codebase, please cite our paper.
@article{peng2017基於子空間映射與多臂吃角子老虎機技術之實數最佳化,
title={基於子空間映射與多臂吃角子老虎機技術之實數最佳化},
author={Peng, Chun-Jen},
journal={臺灣大學電機工程學研究所學位論文},
pages={1--77},
year={2017},
publisher={臺灣大學}
}
Peng, Chun-Jen (Larry) - [email protected]
- Taiwan Evolutionary Intelligence Laboratory
- CMAES: N. Hansen, S. D. Müller, and P. Koumoutsakos. Reducing the time complexity of the derandomized evolution strategy with covariance matrix adaptation (CMA-ES). Evolutionary computation, 11(1):1–18, 2003.
- SPSO: M. Clerc. Standard particle swarm optimisation. Retrieved from https://hal.archives-ouvertes.fr/hal-00764996/, 2012.
- ACOR: K. Socha and M. Dorigo. Ant colony optimization for continuous domains. European journal of operational research, 185(3):1155–1173, 2008.