{"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/generative-adversarial-networks-in-estimation","title":"Generative Adversarial Networks in Estimation of Distribution Algorithms for Combinatorial Optimization","arxiv_id":"1509.09235","date":"2015-09-30","proceeding":null,"authors":["Malte Probst"],"abstract":"Estimation of Distribution Algorithms (EDAs) require flexible probability\nmodels that can be efficiently learned and sampled. Generative Adversarial\nNetworks (GAN) are generative neural networks which can be trained to\nimplicitly model the probability distribution of given data, and it is possible\nto sample this distribution. We integrate a GAN into an EDA and evaluate the\nperformance of this system when solving combinatorial optimization problems\nwith a single objective. We use several standard benchmark problems and compare\nthe results to state-of-the-art multivariate EDAs. GAN-EDA doe not yield\ncompetitive results - the GAN lacks the ability to quickly learn a good\napproximation of the probability distribution. A key reason seems to be the\nlarge amount of noise present in the first EDA generations.","url_abs":"http://arxiv.org/abs/1509.09235v2","url_pdf":"http://arxiv.org/pdf/1509.09235v2.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":"generative-adversarial-networks-in-estimation","repo_url":"https://github.com/wohnjayne/eda-suite","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"none","reach":null}],"tasks":[{"task_slug":"combinatorial-optimization","task_name":"Combinatorial Optimization"}],"methods":[{"method_slug":"convolution","method_name":"Convolution"}],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}