Construction of Mathematical Evaluation System Based on Improved Genetic Algorithm

The traditional mathematical evaluation system often faces problems such as large amount of calculation and unsatisfactory optimization effect. In the execution process of the algorithm, this paper adopts the adaptive crossover and mutation probability strategy, so that the algorithm can automatically adjust the search direction according to the evolutionary state to avoid falling into the local optimal solution. We design a multi-objective optimization mechanism, so that the algorithm can consider multiple evaluation indexes at the same time and realize the optimization of the comprehensive evaluation system. From the perspective of optimal solution quality, Improved Genetic Algorithm (IGA) outperforms Particle Swarm Optimization (PSO) and Simulated Annealing (SA) algorithms in terms of average comprehensive score. This indicates that IGA can more effectively balance and optimize multiple factors and their sub indicators when constructing a mathematical evaluation system. In this paper, by integrating the improved genetic algorithm to construct a mathematical evaluation framework, we provide strong support for education, scientific research and decision support platforms.