Deep Memory Search: A Metaheuristic Approach for Optimizing Heuristic Search
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arXiv
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| Auteurs principaux: | , , , , |
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| Format: | Preprint |
| Publié: |
2024
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| _version_ | 1866913559583129600 |
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| author | Hedar, Abdel-Rahman Abdel-Hakim, Alaa E. Deabes, Wael Alotaibi, Youseef Bouazza, Kheir Eddine |
| author_facet | Hedar, Abdel-Rahman Abdel-Hakim, Alaa E. Deabes, Wael Alotaibi, Youseef Bouazza, Kheir Eddine |
| contents | Metaheuristic search methods have proven to be essential tools for tackling complex optimization challenges, but their full potential is often constrained by conventional algorithmic frameworks. In this paper, we introduce a novel approach called Deep Heuristic Search (DHS), which models metaheuristic search as a memory-driven process. DHS employs multiple search layers and memory-based exploration-exploitation mechanisms to navigate large, dynamic search spaces. By utilizing model-free memory representations, DHS enhances the ability to traverse temporal trajectories without relying on probabilistic transition models. The proposed method demonstrates significant improvements in search efficiency and performance across a range of heuristic optimization problems. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2410_17042 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Deep Memory Search: A Metaheuristic Approach for Optimizing Heuristic Search Hedar, Abdel-Rahman Abdel-Hakim, Alaa E. Deabes, Wael Alotaibi, Youseef Bouazza, Kheir Eddine Artificial Intelligence Machine Learning I.2.6 I.2.6 Metaheuristic search methods have proven to be essential tools for tackling complex optimization challenges, but their full potential is often constrained by conventional algorithmic frameworks. In this paper, we introduce a novel approach called Deep Heuristic Search (DHS), which models metaheuristic search as a memory-driven process. DHS employs multiple search layers and memory-based exploration-exploitation mechanisms to navigate large, dynamic search spaces. By utilizing model-free memory representations, DHS enhances the ability to traverse temporal trajectories without relying on probabilistic transition models. The proposed method demonstrates significant improvements in search efficiency and performance across a range of heuristic optimization problems. |
| title | Deep Memory Search: A Metaheuristic Approach for Optimizing Heuristic Search |
| topic | Artificial Intelligence Machine Learning I.2.6 I.2.6 |
| url | https://arxiv.org/abs/2410.17042 |