{"ID":2839531,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2511.15377","arxiv_id":"2511.15377","title":"Towards Evolutionary Optimization Using the Ising Model","abstract":"In this paper, we study the problem of finding the global minima of a given function. Specifically, we consider complicated functions with numerous local minima, as is often the case for real-world data mining losses. We do so by applying a model from theoretical physics to create an Ising model-based evolutionary optimization algorithm. Our algorithm creates stable regions of local optima and a high potential for improvement between these regions. This enables the accurate identification of global minima, surpassing comparable methods, and has promising applications to ensembles.","short_abstract":"In this paper, we study the problem of finding the global minima of a given function. Specifically, we consider complicated functions with numerous local minima, as is often the case for real-world data mining losses. We do so by applying a model from theoretical physics to create an Ising model-based evolutionary opti...","url_abs":"https://arxiv.org/abs/2511.15377","url_pdf":"https://arxiv.org/pdf/2511.15377v1","authors":"[\"Simon Klüttermann\"]","published":"2025-11-19T12:08:28Z","proceeding":"cs.NE","tasks":"[\"cs.NE\"]","methods":"[]","has_code":false}