Towards Evolutionary Optimization Using the Ising Model
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arXiv
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| Format: | Preprint |
| Veröffentlicht: |
2025
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| _version_ | 1866911276521750528 |
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| author | Klüttermann, Simon |
| author_facet | Klüttermann, Simon |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2511_15377 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Towards Evolutionary Optimization Using the Ising Model Klüttermann, Simon Neural and Evolutionary Computing 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. |
| title | Towards Evolutionary Optimization Using the Ising Model |
| topic | Neural and Evolutionary Computing |
| url | https://arxiv.org/abs/2511.15377 |