Application of an Improved Genetic Programming Optimization Algorithm in the Online College Application System

Query optimization is a very import research question, In the field of distributed database queries, driven by the need to address numerous optimization, it challenges inherent in distributed database systems. The Online College Application System is a distributed query system, where stringent requirements for query accuracy, it efficiency compound the complexity of optimization. To face these challenges, this paper discusses genetic programming optimization algorithm and give a novel query model that incorporates this algorithm. By introducing a penalty factor and leveraging homologous crossover, the algorithm achieves a query tree model with near-minimal execution costs. Experiment results demonstrate, the advantages of the proposed algorithm when applied to the Online College Application System.