Colony-Enhanced Recurrent Neural Architecture Search: Collaborative Ant-Based Optimization

• URL: http://arxiv.org/abs/2401.17480v1
• Date: Tue, 30 Jan 2024 22:27:31 GMT
• Title: Colony-Enhanced Recurrent Neural Architecture Search: Collaborative Ant-Based Optimization
• Authors: Abdelrahman Elsaid
• Abstract summary: This paper introduces Collaborative Ant-based Neural Topology Search (CANTS-N) In this innovative approach, ant-inspired agents meticulously construct neural network structures, dynamically adapting within a dynamic environment. CANTS-N has the potential to reshape the landscape of Neural Architecture Search (NAS) and Neural Evolution (NE)
• Score: 0.0
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: 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.

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