{"id":21507792,"url":"https://github.com/lmarti/nsgaiii","last_synced_at":"2025-09-08T22:42:02.860Z","repository":{"id":43810908,"uuid":"48892597","full_name":"lmarti/nsgaiii","owner":"lmarti","description":"An implementation of NSGA-III in Python.","archived":false,"fork":false,"pushed_at":"2024-06-15T19:04:47.000Z","size":1331,"stargazers_count":121,"open_issues_count":0,"forks_count":52,"subscribers_count":9,"default_branch":"master","last_synced_at":"2025-04-02T05:46:27.743Z","etag":null,"topics":["evolutionary-algorithms","evolutionary-computation","many-objective-optimization","multiobjective-optimization","nsga-iii"],"latest_commit_sha":null,"homepage":"https://github.com/lmarti/nsgaiii","language":"Jupyter Notebook","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":"lgpl-3.0","status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/lmarti.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":"2016-01-01T23:27:31.000Z","updated_at":"2025-04-01T11:34:20.000Z","dependencies_parsed_at":"2024-06-15T19:52:02.187Z","dependency_job_id":null,"html_url":"https://github.com/lmarti/nsgaiii","commit_stats":null,"previous_names":[],"tags_count":2,"template":false,"template_full_name":null,"purl":"pkg:github/lmarti/nsgaiii","repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/lmarti%2Fnsgaiii","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/lmarti%2Fnsgaiii/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/lmarti%2Fnsgaiii/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/lmarti%2Fnsgaiii/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/lmarti","download_url":"https://codeload.github.com/lmarti/nsgaiii/tar.gz/refs/heads/master","sbom_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/lmarti%2Fnsgaiii/sbom","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":270305842,"owners_count":24562109,"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","status":"online","status_checked_at":"2025-08-13T02:00:09.904Z","response_time":66,"last_error":null,"robots_txt_status":"success","robots_txt_updated_at":"2025-07-24T06:49:26.215Z","robots_txt_url":"https://github.com/robots.txt","online":true,"can_crawl_api":true,"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":["evolutionary-algorithms","evolutionary-computation","many-objective-optimization","multiobjective-optimization","nsga-iii"],"created_at":"2024-11-23T20:38:50.928Z","updated_at":"2025-08-13T20:33:25.761Z","avatar_url":"https://github.com/lmarti.png","language":"Jupyter Notebook","funding_links":[],"categories":[],"sub_categories":[],"readme":"# `nsgaiii`: Understanding the Implementation of NSGA-III in Python\n\n[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/lmarti/nsgaiii/blob/master/NSGA-III%20in%20Python.ipynb)\n[![nbviewer](https://raw.githubusercontent.com/jupyter/design/master/logos/Badges/nbviewer_badge.svg)](https://nbviewer.jupyter.org/github/lmarti/nsgaiii/blob/master/NSGA-III%20in%20Python.ipynb)\n\nA Python implementation of the NSGA-III selection algorithm as described in:\n\n- Deb, K., and Jain, H. (2014). *An Evolutionary Many-Objective Optimization Algorithm Using Reference-Point-Based Nondominated Sorting Approach, Part I: Solving Problems With Box Constraints*. IEEE Transactions on Evolutionary Computation, 18(4), 577–601. doi: [10.1109/TEVC.2013.2281535](https://ieeexplore.ieee.org/document/6600851).\n- Jain, H. and Deb, K. (2014). *An Evolutionary Many-Objective Optimization Algorithm Using Reference-Point Based Nondominated Sorting Approach, Part II: Handling Constraints and Extending to an Adaptive Approach*. IEEE Transactions on Evolutionary Computation, 18(4), 602-622. doi: [10.1109/TEVC.2013.2281534](https://ieeexplore.ieee.org/document/6595567).\n\n`nsgaiii` can be used with -as has been developed relying on- the [DEAP module](https://www.github.com/DEAP/deap).\n\nThis code is highly experimental. Contributions and bug fixes are welcome.\n\n⚠️ **Important notice:** The `nsgaiii` code has been integrated in [DEAP](https://github.com/DEAP/deap) as their function [`selNSGA3`](https://github.com/DEAP/deap/blob/master/deap/tools/emo.py#L492). I recommend you use that implementation as it is actively maintained.\n\n## Demonstration\n\nI have prepared a sample [Jupyter/IPython notebook](http://nbviewer.jupyter.org/github/lmarti/nsgaiii/blob/master/NSGA-III%20in%20Python.ipynb) that illustrates NSGA-III.\n\n## Installation\n\nSo far, the only form of installation is to clone the project from GitHub,\n\n```bash\ngit clone https://github.com/lmarti/nsgaiii.git\n```\n\n...and then installing it by running:\n\n```bash\npython setup.py install\n```\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Flmarti%2Fnsgaiii","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Flmarti%2Fnsgaiii","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Flmarti%2Fnsgaiii/lists"}