{"id":15645755,"url":"https://github.com/hvass-labs/metaops","last_synced_at":"2025-07-26T14:32:27.542Z","repository":{"id":129938076,"uuid":"145686659","full_name":"Hvass-Labs/MetaOps","owner":"Hvass-Labs","description":"Tuning the Parameters of Heuristic Optimizers (Meta-Optimization / Hyper-Parameter Optimization)","archived":false,"fork":false,"pushed_at":"2018-10-23T14:42:57.000Z","size":1186,"stargazers_count":54,"open_issues_count":0,"forks_count":12,"subscribers_count":5,"default_branch":"master","last_synced_at":"2025-04-30T10:44:59.046Z","etag":null,"topics":["blackbox-optimization","differential-evolution","hyperparameter-optimization","meta-heuristic","nsga2","particle-swarm-optimization","random-search"],"latest_commit_sha":null,"homepage":"","language":"Jupyter Notebook","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":"mit","status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/Hvass-Labs.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":"LICENSE","code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null,"governance":null,"roadmap":null,"authors":null,"dei":null,"publiccode":null,"codemeta":null}},"created_at":"2018-08-22T09:27:32.000Z","updated_at":"2025-03-14T02:01:13.000Z","dependencies_parsed_at":"2023-04-04T01:32:37.742Z","dependency_job_id":null,"html_url":"https://github.com/Hvass-Labs/MetaOps","commit_stats":null,"previous_names":[],"tags_count":0,"template":false,"template_full_name":null,"purl":"pkg:github/Hvass-Labs/MetaOps","repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Hvass-Labs%2FMetaOps","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Hvass-Labs%2FMetaOps/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Hvass-Labs%2FMetaOps/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Hvass-Labs%2FMetaOps/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/Hvass-Labs","download_url":"https://codeload.github.com/Hvass-Labs/MetaOps/tar.gz/refs/heads/master","sbom_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Hvass-Labs%2FMetaOps/sbom","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":261080562,"owners_count":23106597,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2022-07-04T15:15:14.044Z","host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":["blackbox-optimization","differential-evolution","hyperparameter-optimization","meta-heuristic","nsga2","particle-swarm-optimization","random-search"],"created_at":"2024-10-03T12:09:45.155Z","updated_at":"2025-06-21T07:05:32.237Z","avatar_url":"https://github.com/Hvass-Labs.png","language":"Jupyter Notebook","funding_links":[],"categories":[],"sub_categories":[],"readme":"# MetaOps\n\n[Original repository on GitHub](https://github.com/Hvass-Labs/MetaOps)\n\nOriginal author is [Magnus Erik Hvass Pedersen](http://www.hvass-labs.org)\n\n\n## Introduction\n\nThis is a small collection of research papers on automatic tuning of the parameters\nof a heuristic optimizer such as\n[Genetic Algorithm](https://en.wikipedia.org/wiki/Genetic_algorithm),\n[Particle Swarm Optimization](https://en.wikipedia.org/wiki/Particle_swarm_optimization),\nand [Differential Evolution](https://en.wikipedia.org/wiki/Differential_evolution).\nThe parameter tuning is done by another overlaying optimizer and this is often called\n[Meta-Optimization](https://en.wikipedia.org/wiki/Meta-optimization)\nor [Hyper-Parameter Optimization](https://en.wikipedia.org/wiki/Hyperparameter_optimization).\n\n\n## Papers\n\n1. Bayesian Meta-Optimization ([Notebook](https://github.com/Hvass-Labs/MetaOps/blob/master/01_Bayesian_Meta-Optimization.ipynb)) ([Google Colab](https://colab.research.google.com/github/Hvass-Labs/MetaOps/blob/master/01_Bayesian_Meta-Optimization.ipynb))\n\n2. Multi-Objective Meta-Optimization ([Notebook](https://github.com/Hvass-Labs/MetaOps/blob/master/02_Multi-Objective_Meta-Optimization.ipynb)) ([Google Colab](https://colab.research.google.com/github/Hvass-Labs/MetaOps/blob/master/02_Multi-Objective_Meta-Optimization.ipynb))\n\n3. Meta-Optimization Using Many Problems ([Notebook](https://github.com/Hvass-Labs/MetaOps/blob/master/03_Meta-Optimization_Using_Many_Problems.ipynb)) ([Google Colab](https://colab.research.google.com/github/Hvass-Labs/MetaOps/blob/master/03_Meta-Optimization_Using_Many_Problems.ipynb))\n\n\n## Videos\n\nThere is a [YouTube video](https://www.youtube.com/playlist?list=PL9Hr9sNUjfsl1877BS8m3yt8t_wq2IWji) for each research paper.\n\n\n## Downloading\n\nIt is recommended that you download the whole repository from GitHub,\ninstead of just downloading the individual Python Notebooks.\n\n\n### Git\n\nThe easiest way to download and install this is by using git from the command-line:\n\n    git clone https://github.com/Hvass-Labs/MetaOps.git\n\nThis creates the directory `MetaOps` and downloads all the files to it.\n\nThis also makes it easy to update the files, simply by executing this command inside that directory:\n\n    git pull\n\n\n### Zip-File\n\nYou can also [download](https://github.com/Hvass-Labs/MetaOps/archive/master.zip)\nthe contents of the GitHub repository as a Zip-file and extract it manually.\n\n\n## How To Run\n\nIf you want to edit and run the Notebooks on your own computer, then it is\nsuggested that you use the [Anaconda](https://www.anaconda.com/download)\ndistribution with **Python 3.6** (or later) because it has most of the required\npackages already installed. Then you type the following commands in a terminal\nwindow:\n\n    cd MetaOps\n    conda create --name metaops python=3.6\n    source activate metaops\n    pip install -r requirements.txt\n\nNow you can run the Notebooks by typing this command:\n\n    jupyter notebook\n\n\n### Run in Google Colab\n\nIf you do not want to install anything on your own computer, then the Notebooks\ncan be viewed, edited and run entirely on the internet by using\n[Google Colab](https://colab.research.google.com).\nYou can click the \"Google Colab\"-link next to the research papers listed above.\nYou can view the Notebook on Colab but in order to run it you need to login using\nyour Google account.\nThen you need to execute the following commands at the top of the Notebook,\nwhich clones MetaOps to your work-directory on Colab and installs the required\npackages.\n\n    import os\n    work_dir = \"/content/MetaOps/\"\n    if os.getcwd() != work_dir:\n        !git clone https://github.com/Hvass-Labs/MetaOps.git\n    os.chdir(work_dir)\n    !pip install -r requirements.txt\n\n\n## License (MIT)\n\nThese Python Notebooks and source-code are published under the [MIT License](https://github.com/Hvass-Labs/MetaOps/blob/master/LICENSE)\nwhich allows very broad use for both academic and commercial purposes.\n\nYou are very welcome to modify and use the source-code in your own project.\nPlease keep a link to the [original repository](https://github.com/Hvass-Labs/MetaOps).\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fhvass-labs%2Fmetaops","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fhvass-labs%2Fmetaops","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fhvass-labs%2Fmetaops/lists"}