{"id":29789310,"url":"https://github.com/kb2623/cec2005real","last_synced_at":"2026-05-19T04:07:33.650Z","repository":{"id":306408167,"uuid":"1024971212","full_name":"kb2623/cec2005real","owner":"kb2623","description":"Problem for the CEC 2005 Special Session on Real-Parameter Optimization","archived":false,"fork":false,"pushed_at":"2025-07-25T09:19:28.000Z","size":2198,"stargazers_count":0,"open_issues_count":0,"forks_count":0,"subscribers_count":0,"default_branch":"main","last_synced_at":"2025-07-25T15:28:56.631Z","etag":null,"topics":["benchmark-functions","c","cec2005","optimization","python","real-parameter-optimization"],"latest_commit_sha":null,"homepage":"","language":"C","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":null,"status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/kb2623.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":null,"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,"zenodo":null}},"created_at":"2025-07-23T14:20:02.000Z","updated_at":"2025-07-25T10:23:41.000Z","dependencies_parsed_at":"2025-07-25T15:41:26.976Z","dependency_job_id":null,"html_url":"https://github.com/kb2623/cec2005real","commit_stats":null,"previous_names":["kb2623/cec2005real"],"tags_count":null,"template":false,"template_full_name":null,"purl":"pkg:github/kb2623/cec2005real","repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/kb2623%2Fcec2005real","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/kb2623%2Fcec2005real/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/kb2623%2Fcec2005real/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/kb2623%2Fcec2005real/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/kb2623","download_url":"https://codeload.github.com/kb2623/cec2005real/tar.gz/refs/heads/main","sbom_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/kb2623%2Fcec2005real/sbom","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":267431557,"owners_count":24086083,"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":"2025-07-27T02:00:11.917Z","response_time":82,"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":["benchmark-functions","c","cec2005","optimization","python","real-parameter-optimization"],"created_at":"2025-07-27T22:07:39.525Z","updated_at":"2026-05-19T04:07:33.643Z","avatar_url":"https://github.com/kb2623.png","language":"C","funding_links":[],"categories":[],"sub_categories":[],"readme":"# Introduction\n\nThis is a Python wrapping using the C++ Implementation of the test suite for the Special Session on Large Scale Global Optimization at 2005 IEEE Congress on Evolutionary Computation.\n\n## Note\n\nIf you are to use any part of this code, please cite the following publications:\n\nP. N. Suganthan, N. Hansen, J. J. Liang, K. Deb, Y.-P. Chen, A. Auger and S. Tiwari, \"Problem Definitions and Evaluation Criteria for the CEC 2005 Special Session on Real-Parameter Optimization\", Technical Report, Nanyang Technological University, Singapore, May 2005 AND KanGAL Report #2005005, IIT Kanpur, India.\n\nhttp://www3.ntu.edu.sg/home/EPNSugan/index_files/CEC-05/CEC05.htm   \n\n## Requirements\n\n* GNU gcc\n* Python\n* Cython\n\n## Testing Environment\n\n* buntu noble/sid\n* gcc 13.3.0\n* Python 3.13\n\n## Quickstart\n\nThe package is very simple to use. There is a class Function with two functions:\n\n* Give information for each function: their optimum, their dimensionality, the domain search, and the expected threshold to achieve the optima.\n* Give a fitness function to evaluate solutions. It expect that these solutions are numpy arrays (vectors) but it can also work with normal arrays.\n\nThese two functionalities are done with two methods in Benchmark class:\n\n- **get_num_functions()**\n\n  Return the number of functions in the benchmarks (15)\n\n- **get_info()**\n\n  Return an array with the following information, where /function_id/ is the identifier of the function, a int value between 1 and 15.\n\n    - lower, upper\n        *lower* and *upper* boundaries of the domain search. \n\n    - best\n        Optimum to achieve, it is always zero, thus it can be ignored.\n\n    - threshold\n        Threshold to obtain, it is always zero, thus it can also be ignored.\n\n    - dimension\n        Dimension for the function, it is always 1000.\n\n    It can be noticed that several data are the same for all functions. It is made for maintaining the \n    same interface to other cec20xx competitions.\n\n- **get_eval_function()**\n\n  It returns the fitness function to evaluate the solutions.\n\n## Examples of use\n\n### Obtain information about one function\n\n```python\n\u003e\u003e\u003e from cec2005real.cec2005real import Function\n\u003e\u003e\u003e fbench = Function(1, 10)\n\u003e\u003e\u003e fbench.info()\n{'best': 0.0,\n 'dimension': 1000,\n 'lower': -100.0,\n 'threshold': 0,\n 'upper': 100.0}\n```\n\n### Create random solution for the search\n\n```python\n\u003e\u003e\u003e from numpy.random import rand\n\u003e\u003e\u003e info = fbench.info()\n\u003e\u003e\u003e dim = info['dimension']\n\u003e\u003e\u003e sol = info['lower'] + rand(dim) * (info['upper'] - info['lower'])\n```\n\n### Evaluate a solution\n\n```python\n\u003e\u003e\u003e fbench.eval(sol)\n464006824710.75995\n```\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fkb2623%2Fcec2005real","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fkb2623%2Fcec2005real","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fkb2623%2Fcec2005real/lists"}