A diversity-enhanced genetic algorithm for efficient exploration of parameter spaces
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arXiv
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| Auteurs principaux: | , |
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| Format: | Preprint |
| Publié: |
2024
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| _version_ | 1866929644592168960 |
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| author | Wessén, Jonas Camargo-Molina, Eliel |
| author_facet | Wessén, Jonas Camargo-Molina, Eliel |
| contents | We present a Python package together with a practical guide for the implementation of a lightweight diversity-enhanced genetic algorithm (GA) approach for the exploration of multi-dimensional parameter spaces. Searching a parameter space for regions with desirable properties, e.g. compatibility with experimental data, poses a type of optimization problem wherein the focus lies on pinpointing all "good enough" solutions, rather than a single "best solution". Our approach dramatically outperforms random scans and other GA-based implementations in this aspect. We validate the effectiveness of our approach by applying it to a particle physics problem, showcasing its ability to identify promising parameter points in isolated, viable regions meeting experimental constraints. The companion Python package is applicable to optimization problems beyond those considered in this work, including scanning over discrete parameters (categories). A detailed guide for its usage is provided. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2412_17104 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | A diversity-enhanced genetic algorithm for efficient exploration of parameter spaces Wessén, Jonas Camargo-Molina, Eliel Neural and Evolutionary Computing High Energy Physics - Phenomenology We present a Python package together with a practical guide for the implementation of a lightweight diversity-enhanced genetic algorithm (GA) approach for the exploration of multi-dimensional parameter spaces. Searching a parameter space for regions with desirable properties, e.g. compatibility with experimental data, poses a type of optimization problem wherein the focus lies on pinpointing all "good enough" solutions, rather than a single "best solution". Our approach dramatically outperforms random scans and other GA-based implementations in this aspect. We validate the effectiveness of our approach by applying it to a particle physics problem, showcasing its ability to identify promising parameter points in isolated, viable regions meeting experimental constraints. The companion Python package is applicable to optimization problems beyond those considered in this work, including scanning over discrete parameters (categories). A detailed guide for its usage is provided. |
| title | A diversity-enhanced genetic algorithm for efficient exploration of parameter spaces |
| topic | Neural and Evolutionary Computing High Energy Physics - Phenomenology |
| url | https://arxiv.org/abs/2412.17104 |