Optimization of Industry-Education Integration Architecture in Digital Economy Based on Genetic Algorithm

With the rapid development of the digital economy, the integration of industry and education has become an important way to promote collaboration between universities and enterprises. However, the current technology architecture of industry-education integration still suffers from significant shortcomings in resource allocation efficiency, system adaptability, and flexibility. This study proposes a technical architecture optimization method based on genetic algorithms to address these challenges, improving resource allocation efficiency and platform performance. Through the construction of a resource allocation model and the design of a fitness function, the genetic algorithm realizes balanced allocation and dynamic adjustment of resources via iterative optimization. Experimental results show that the optimized architecture significantly improves resource utilization (by 20%), reduces task completion time (by 25%), and enhances collaboration efficiency (by 30%). Furthermore, the architecture exhibits strong flexibility and scalability, maintaining high performance under dynamic resource requirements and expanded cooperation scales. These findings validate the effectiveness of genetic algorithms in optimizing the technological architecture of industry-education integration.