{"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/denoising-autoencoders-for-fast-combinatorial","title":"Denoising Autoencoders for fast Combinatorial Black Box Optimization","arxiv_id":"1503.01954","date":"2015-03-06","proceeding":null,"authors":["Malte Probst"],"abstract":"Estimation of Distribution Algorithms (EDAs) require flexible probability\nmodels that can be efficiently learned and sampled. Autoencoders (AE) are\ngenerative stochastic networks with these desired properties. We integrate a\nspecial type of AE, the Denoising Autoencoder (DAE), into an EDA and evaluate\nthe performance of DAE-EDA on several combinatorial optimization problems with\na single objective. We asses the number of fitness evaluations as well as the\nrequired CPU times. We compare the results to the performance to the Bayesian\nOptimization Algorithm (BOA) and RBM-EDA, another EDA which is based on a\ngenerative neural network which has proven competitive with BOA. For the\nconsidered problem instances, DAE-EDA is considerably faster than BOA and\nRBM-EDA, sometimes by orders of magnitude. The number of fitness evaluations is\nhigher than for BOA, but competitive with RBM-EDA. These results show that DAEs\ncan be useful tools for problems with low but non-negligible fitness evaluation\ncosts.","url_abs":"http://arxiv.org/abs/1503.01954v2","url_pdf":"http://arxiv.org/pdf/1503.01954v2.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":"denoising-autoencoders-for-fast-combinatorial","repo_url":"https://github.com/wohnjayne/eda-suite","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":null}],"tasks":[{"task_slug":"bayesian-optimization","task_name":"Bayesian Optimization"},{"task_slug":null,"task_name":"CPU"},{"task_slug":"combinatorial-optimization","task_name":"Combinatorial Optimization"},{"task_slug":"denoising","task_name":"Denoising"}],"methods":[{"method_slug":"ae","method_name":"AE"},{"method_slug":"denoising-autoencoder","method_name":"Denoising Autoencoder"}],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}