A Reinforced Evolution-Based Approach to Multi-Resource Load Balancing
Fuente:
arXiv
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| Autor principal: | |
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| Formato: | Preprint |
| Publicado: |
2025
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| Acceso en línea: | |
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| _version_ | 1866917064697970688 |
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| author | Sliwko, Leszek |
| author_facet | Sliwko, Leszek |
| contents | This paper presents a reinforced genetic approach to a defined d-resource system optimization problem. The classical evolution schema was ineffective due to a very strict feasibility function in the studied problem. Hence, the presented strategy has introduced several modifications and adaptations to standard genetic routines, e.g.: a migration operator which is an analogy to the biological random genetic drift. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2511_04183 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | A Reinforced Evolution-Based Approach to Multi-Resource Load Balancing Sliwko, Leszek Neural and Evolutionary Computing Artificial Intelligence Distributed, Parallel, and Cluster Computing This paper presents a reinforced genetic approach to a defined d-resource system optimization problem. The classical evolution schema was ineffective due to a very strict feasibility function in the studied problem. Hence, the presented strategy has introduced several modifications and adaptations to standard genetic routines, e.g.: a migration operator which is an analogy to the biological random genetic drift. |
| title | A Reinforced Evolution-Based Approach to Multi-Resource Load Balancing |
| topic | Neural and Evolutionary Computing Artificial Intelligence Distributed, Parallel, and Cluster Computing |
| url | https://arxiv.org/abs/2511.04183 |