Smart Starts: Accelerating Convergence through Uncommon Region Exploration
Fuente:
arXiv
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| Autori principali: | , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| Soggetti: | |
| Accesso online: | |
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| _version_ | 1866908355719593984 |
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| author | Zhang, Xinyu Antunes, Mário Estro, Tyler Zadok, Erez Mueller, Klaus |
| author_facet | Zhang, Xinyu Antunes, Mário Estro, Tyler Zadok, Erez Mueller, Klaus |
| contents | Initialization profoundly affects evolutionary algorithm (EA) efficacy by dictating search trajectories and convergence. This study introduces a hybrid initialization strategy combining empty-space search algorithm (ESA) and opposition-based learning (OBL). OBL initially generates a diverse population, subsequently augmented by ESA, which identifies under-explored regions. This synergy enhances population diversity, accelerates convergence, and improves EA performance on complex, high-dimensional optimization problems. Benchmark results demonstrate the proposed method's superiority in solution quality and convergence speed compared to conventional initialization techniques. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2505_05661 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Smart Starts: Accelerating Convergence through Uncommon Region Exploration Zhang, Xinyu Antunes, Mário Estro, Tyler Zadok, Erez Mueller, Klaus Neural and Evolutionary Computing Initialization profoundly affects evolutionary algorithm (EA) efficacy by dictating search trajectories and convergence. This study introduces a hybrid initialization strategy combining empty-space search algorithm (ESA) and opposition-based learning (OBL). OBL initially generates a diverse population, subsequently augmented by ESA, which identifies under-explored regions. This synergy enhances population diversity, accelerates convergence, and improves EA performance on complex, high-dimensional optimization problems. Benchmark results demonstrate the proposed method's superiority in solution quality and convergence speed compared to conventional initialization techniques. |
| title | Smart Starts: Accelerating Convergence through Uncommon Region Exploration |
| topic | Neural and Evolutionary Computing |
| url | https://arxiv.org/abs/2505.05661 |