{"id":21447232,"url":"https://github.com/lcbx/algo_contest","last_synced_at":"2025-03-17T01:43:45.715Z","repository":{"id":83149105,"uuid":"185219240","full_name":"Lcbx/algo_contest","owner":"Lcbx","description":"This was done in a contest in algorithm design held for the INF8775 (analysis of algorithms) course of Polytechnique Montreal","archived":false,"fork":false,"pushed_at":"2021-11-14T17:46:14.000Z","size":278,"stargazers_count":0,"open_issues_count":0,"forks_count":0,"subscribers_count":2,"default_branch":"master","last_synced_at":"2025-01-23T11:34:29.882Z","etag":null,"topics":[],"latest_commit_sha":null,"homepage":null,"language":"Python","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/Lcbx.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}},"created_at":"2019-05-06T15:03:04.000Z","updated_at":"2021-11-14T17:46:17.000Z","dependencies_parsed_at":null,"dependency_job_id":"bd315519-61bc-49c4-940a-dc43ecceb13e","html_url":"https://github.com/Lcbx/algo_contest","commit_stats":null,"previous_names":[],"tags_count":0,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Lcbx%2Falgo_contest","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Lcbx%2Falgo_contest/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Lcbx%2Falgo_contest/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Lcbx%2Falgo_contest/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/Lcbx","download_url":"https://codeload.github.com/Lcbx/algo_contest/tar.gz/refs/heads/master","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":243960449,"owners_count":20375101,"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":[],"created_at":"2024-11-23T03:09:25.398Z","updated_at":"2025-03-17T01:43:45.708Z","avatar_url":"https://github.com/Lcbx.png","language":"Python","funding_links":[],"categories":[],"sub_categories":[],"readme":"This was done in a contest in algorithm design held for the INF8775 (analysis of algorithms) course of Polytechnique Montreal      \n\n\n\nComme le veut la tradition, je vous présente les trois meilleurs algorithmes pour le concours du TP3. Les exemplaires choisis sont les suivants :  \n- LEGO_50_50_1000  \n- LEGO_50_100_2000  \n- LEGO_100_100_2000    \n\n\n#1. Antoine Daigneault-Demers et James Lok  \nScores : 1294, 698, 3119  \nCet algorithme trouve une solution de base avec un algorithme glouton, puis utilise un mélange de recherche par voisinage variable et de recherche tabou afin d’améliorer la solution initiale.    \n\n\n#2. Kim Piché et Abderahmane Bouziane  \nScores : 1293, 719, 3225  \nAlgorithme génétique classique prenant en compte plusieurs paramètres.    \n\n\n#3. Grégoire Dervaux et Marc Lanovaz  \nScores : 1371, 939, 3314  \nUn algorithme glouton est utilisé pour trouver une solution initiale, qui est ensuite améliorée avec une heuristique d'amélioration locale. L'algorithme est parallélisé.      \n\n\n#Moi (original)  \nScores : 2147, 2197, 6749  \nUn algorithme d'approximation (algorithme NNLS de Lawson-Hanson) est utilisé puis affiné par recherche par voisinage aléatoire.    \n\n#Moi (modifie)  \nScores : 1364, 1050, 3734  \nUn algorithme d'approximation (algorithme NNLS de Lawson-Hanson) est utilisé puis affiné par recherche par voisinage aléatoire avec tabou et temperature.  \n\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Flcbx%2Falgo_contest","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Flcbx%2Falgo_contest","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Flcbx%2Falgo_contest/lists"}