Design and Implementation of Training Plan Optimization for Athletes in Track and Field Competitions Using A Genetic Algorithm

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Zuo Tian, Jinlan Wang


This study explores the design and implementation of genetic algorithm (GA)-optimized training plans for track and field athletes, aiming to enhance performance outcomes while minimizing the risk of injury and overtraining. The research methodology involves defining a comprehensive fitness function that integrates multiple performance metrics, including sprint times, jump distances, throwing distances, and endurance metrics. A total of 30 athletes participated in the study, with performance data collected before and after implementing the GA-optimized training plans. Statistical analysis revealed significant improvements in all tracked metrics following the intervention, validating the efficacy of personalized, data-driven training regimens. Contextualizing the findings within the existing literature on sports training optimization methodologies, this study contributes novel insights into the application of GA techniques in track and field athletics. Despite limitations, including sample size and short-term intervention duration, the results underscore the potential of advanced computational methods to revolutionize athletic training strategies. Future research directions include longitudinal studies, incorporation of real-time feedback mechanisms, and interdisciplinary collaborations to further refine optimization algorithms and enhance athletic performance.

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