Abstract
ABSTRACT Introduction: The genetic algorithm is one of the essential theoretical mathematical models for simulating biological development. It is widely used in many fields such as engineering, medicine, and economics. Objective: Use the genetic algorithm as a mathematical model basis for optimization in the high school students’ aptitude program. Methods: The selection method by competition is adopted to elect the random crossover of male crossover probability with high similarity to generate a new population. A genetic algorithm was proposed to adjust the crossover probability and dynamic mutation according to fitness, aiming to solve the problem of dynamic changes. A comparative analysis is performed between the nonlinear differential equations and the Levenberg–Marquardt method algorithm. Results: The algorithm improvement was obtained after analyzing the operation process and structuring of the traditional genetic algorithm; the mathematical model application revealed improvement in the motion accuracy model established by the genetic algorithm. Conclusion: The physical enhancement optimization scheme was tested and verified by a genetic algorithm and proves the research results hold theoretical feasibility. Evidence Level II; Therapeutic Studies – Investigating the results.
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