Research on Competitive Evaluation Based on Genetic Algorithm

Genetic algorithms are machine learning algorithms inspired by the theory of natural evolution. By mimicking the process of natural selection and reproduction, genetic algorithms can provide high-quality solutions to a wide range of problems involving search, optimization, and learning. It can overcome some of the obstacles encountered by traditional search and optimization algorithms, especially for problems with many parameters and complex mathematical representations. To extend its application, this paper applies it to more subjective judging sessions. Firstly, after in-depth analysis, collation, and classification of judging data from several innovation competitions, genetic algorithms are subsequently used to optimize the allocation of works and judging experts, and the current standard score judging scheme is improved by non-linear correction of parameters. The results of the final data experiment show that the model greatly improves the concentration of scores and judging power and provides a reference for similar subjective scenario modelling optimization.