{"ID":2845248,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2511.04183","arxiv_id":"2511.04183","title":"A Reinforced Evolution-Based Approach to Multi-Resource Load Balancing","abstract":"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.","short_abstract":"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 r...","url_abs":"https://arxiv.org/abs/2511.04183","url_pdf":"https://arxiv.org/pdf/2511.04183v1","authors":"[\"Leszek Sliwko\"]","published":"2025-11-06T08:35:56Z","proceeding":"cs.NE","tasks":"[\"cs.NE\",\"cs.AI\",\"cs.DC\"]","methods":"[]","has_code":false}