{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/boosting-binary-optimization-via-binary","title":"Boosting Binary Optimization via Binary Classification: A Case Study of Job Shop Scheduling","arxiv_id":"1808.10813","date":"2018-08-31","proceeding":null,"authors":["Oleg V. Shylo","Hesam Shams"],"abstract":"Many optimization techniques evaluate solutions consecutively, where the next\ncandidate for evaluation is determined by the results of previous evaluations.\nFor example, these include iterative methods, \"black box\" optimization\nalgorithms, simulated annealing, evolutionary algorithms and tabu search, to\nname a few. When solving an optimization problem, these algorithms evaluate a\nlarge number of solutions, which raises the following question: Is it possible\nto learn something about the optimum using these solutions? In this paper, we\ndefine this \"learning\" question in terms of a logistic regression model and\nexplore its predictive accuracy computationally. The proposed model uses a\ncollection of solutions to predict the components of the optimal solutions. To\nillustrate the utility of such predictions, we embed the logistic regression\nmodel into the tabu search algorithm for job shop scheduling problem. The\nresulting framework is simple to implement, yet provides a significant boost to\nthe performance of the standard tabu search.","url_abs":"http://arxiv.org/abs/1808.10813v1","url_pdf":"http://arxiv.org/pdf/1808.10813v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"boosting-binary-optimization-via-binary","repo_url":"https://github.com/quasiquasar/gta-jobshop-data","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"none","reach":null},{"paper_slug":"boosting-binary-optimization-via-binary","repo_url":"https://github.com/thomasWeise/jsspInstancesAndResults","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":null}],"tasks":[{"task_slug":"binary-classification","task_name":"Binary Classification"},{"task_slug":"evolutionary-algorithms","task_name":"Evolutionary Algorithms"},{"task_slug":"classification","task_name":"General Classification"},{"task_slug":"job-shop-scheduling","task_name":"Job Shop Scheduling"},{"task_slug":"scheduling","task_name":"Scheduling"},{"task_slug":"regression-1","task_name":"regression"}],"methods":[{"method_slug":"logistic-regression","method_name":"Logistic Regression"}],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":null,"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}