Colony-Enhanced Recurrent Neural Architecture Search: Collaborative Ant-Based Optimization
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arXiv
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| Formato: | Preprint |
| Publicado: |
2024
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| _version_ | 1866910312023719936 |
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| author | Elsaid, Abdelrahman |
| author_facet | Elsaid, Abdelrahman |
| contents | Crafting neural network architectures manually is a formidable challenge often leading to suboptimal and inefficient structures. The pursuit of the perfect neural configuration is a complex task, prompting the need for a metaheuristic approach such as Neural Architecture Search (NAS). Drawing inspiration from the ingenious mechanisms of nature, this paper introduces Collaborative Ant-based Neural Topology Search (CANTS-N), pushing the boundaries of NAS and Neural Evolution (NE). In this innovative approach, ant-inspired agents meticulously construct neural network structures, dynamically adapting within a dynamic environment, much like their natural counterparts. Guided by Particle Swarm Optimization (PSO), CANTS-N's colonies optimize architecture searches, achieving remarkable improvements in mean squared error (MSE) over established methods, including BP-free CANTS, BP CANTS, and ANTS. Scalable, adaptable, and forward-looking, CANTS-N has the potential to reshape the landscape of NAS and NE. This paper provides detailed insights into its methodology, results, and far-reaching implications. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2401_17480 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Colony-Enhanced Recurrent Neural Architecture Search: Collaborative Ant-Based Optimization Elsaid, Abdelrahman Neural and Evolutionary Computing Crafting neural network architectures manually is a formidable challenge often leading to suboptimal and inefficient structures. The pursuit of the perfect neural configuration is a complex task, prompting the need for a metaheuristic approach such as Neural Architecture Search (NAS). Drawing inspiration from the ingenious mechanisms of nature, this paper introduces Collaborative Ant-based Neural Topology Search (CANTS-N), pushing the boundaries of NAS and Neural Evolution (NE). In this innovative approach, ant-inspired agents meticulously construct neural network structures, dynamically adapting within a dynamic environment, much like their natural counterparts. Guided by Particle Swarm Optimization (PSO), CANTS-N's colonies optimize architecture searches, achieving remarkable improvements in mean squared error (MSE) over established methods, including BP-free CANTS, BP CANTS, and ANTS. Scalable, adaptable, and forward-looking, CANTS-N has the potential to reshape the landscape of NAS and NE. This paper provides detailed insights into its methodology, results, and far-reaching implications. |
| title | Colony-Enhanced Recurrent Neural Architecture Search: Collaborative Ant-Based Optimization |
| topic | Neural and Evolutionary Computing |
| url | https://arxiv.org/abs/2401.17480 |