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Application and optimization of adaptive genetic algorithm in fencing training load prediction: a data

Ya-Nan Jia1

  • 1Nanjing Sport Institute Nanjing, 210014, Jiangsu, China. 13951762758@163.com.

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|January 7, 2026
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Summary

An Adaptive Genetic Algorithm (AGA) accurately predicts fencing training load using sensor data. This model offers efficient, data-driven insights for training monitoring and planning in sports.

Keywords:
Adaptive genetic algorithmData visualizationFencing training loadPredictive model optimizationTime series analysis

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Area of Science:

  • Sports Science
  • Biomechanical Engineering
  • Computational Intelligence

Background:

  • Accurate prediction of training load is crucial for optimizing athletic performance and preventing injuries in fencing.
  • Existing methods for training load assessment may not fully capture the dynamic and multifaceted nature of fencing demands.
  • Sensor-based external mechanical load data provides a objective measure for quantifying training intensity.

Purpose of the Study:

  • To develop and validate an Adaptive Genetic Algorithm (AGA) model for predicting multifaceted training load in fencing.
  • To compare the predictive performance of the AGA model against established machine learning algorithms.
  • To demonstrate the practical utility of the AGA for data-driven training monitoring and planning.

Main Methods:

  • Utilized the Daily and Sports Activities dataset comprising sensor-derived external mechanical load from eight healthy adults performing diverse activities.
  • Mapped time-series activity data to fencing-specific load patterns, categorizing load into strength, aerobic, capacity, endurance, speed, agility, and flexibility.
  • Developed an Adaptive Genetic Algorithm (AGA) that dynamically optimizes fitness function, crossover, and mutation rates for enhanced prediction accuracy.

Main Results:

  • The AGA model consistently outperformed Deep Neural Network with Gated Recurrent Unit (DNN-GRU), Extreme Gradient Boosting (XGBoost), Long Short-Term Memory with Attention Mechanism (LSTM-Attn), Event Adversarial Neural Network (EANN), and Temporal Attention Graph Convolutional Network (TA-GCN).
  • In endurance load prediction, the AGA achieved an R² of 0.97 and an accuracy of 0.96 on the test set.
  • Time-series visualizations identified typical and extreme training load segments, with extreme load defined as predictions within the top decile.

Conclusions:

  • The Adaptive Genetic Algorithm (AGA) framework provides a reliable and computationally efficient method for predicting training load in fencing.
  • The AGA's dynamic optimization capabilities lead to superior prediction accuracy compared to other advanced models.
  • This data-driven approach offers a valuable tool for personalized training monitoring and strategic planning in fencing and similar sports.