{"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/improved-evolutionary-algorithms-for","title":"Improved Evolutionary Algorithms for Submodular Maximization with Cost Constraints","arxiv_id":"2405.05942","date":"2024-05-09","proceeding":null,"authors":["Yanhui Zhu","Samik Basu","A Pavan"],"abstract":"We present an evolutionary algorithm evo-SMC for the problem of Submodular Maximization under Cost constraints (SMC). Our algorithm achieves $1/2$-approximation with a high probability $1-1/n$ within $\\mathcal{O}(n^2K_{\\beta})$ iterations, where $K_{\\beta}$ denotes the maximum size of a feasible solution set with cost constraint $\\beta$. To the best of our knowledge, this is the best approximation guarantee offered by evolutionary algorithms for this problem. We further refine evo-SMC, and develop st-evo-SMC. This stochastic version yields a significantly faster algorithm while maintaining the approximation ratio of $1/2$, with probability $1-\\epsilon$. The required number of iterations reduces to $\\mathcal{O}(nK_{\\beta}\\log{(1/\\epsilon)}/p)$, where the user defined parameters $p \\in (0,1]$ represents the stochasticity probability, and $\\epsilon \\in (0,1]$ denotes the error threshold. Finally, the empirical evaluations carried out through extensive experimentation substantiate the efficiency and effectiveness of our proposed algorithms. Our algorithms consistently outperform existing methods, producing higher-quality solutions.","url_abs":"https://arxiv.org/abs/2405.05942v2","url_pdf":"https://arxiv.org/pdf/2405.05942v2.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":"links_only","authors_date_abstract":"arXiv metadata, CC0 1.0 (https://info.arxiv.org/help/license), from the Kaggle arXiv metadata snapshot of 2026-09-12"},"code_links":[{"paper_slug":"improved-evolutionary-algorithms-for","repo_url":"https://github.com/yz24/evo-smc","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"none","reach":{"status":"ok"}}],"tasks":[],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}