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Barnes Maze Testing Strategies with Small and Large Rodent Models
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Improve the Hunger Games search algorithm to optimize the GoogleNet model.

Yanqiu Li1, Shizheng Qu2, Huan Liu1

  • 1School of Data Science and Artificial Intelligence, Jilin Engineering Normal University, Changchun, China.

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|August 16, 2024
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Summary
This summary is machine-generated.

This study introduces an improved Hunger Games Search (ATHGS) algorithm for optimizing neural network parameters. The ATHGS-GoogleNet model achieves high accuracy (98.1%), demonstrating superior performance in adaptive parameter tuning.

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Area of Science:

  • Artificial Intelligence
  • Machine Learning
  • Deep Learning

Background:

  • Manual neural network parameter tuning is time-consuming and inefficient.
  • Finding optimal parameter combinations manually is challenging.
  • Effective parameter optimization is crucial for neural network performance.

Purpose of the Study:

  • To develop an adaptive method for optimizing neural network parameters.
  • To propose an improved Hunger Games Search algorithm (ATHGS).
  • To introduce a novel ATHGS-GoogleNet model for enhanced performance.

Main Methods:

  • Integrated adaptive weights and chaos mapping into the Hunger Games Search algorithm to create ATHGS.
  • Utilized the ATHGS algorithm to optimize GoogleNet parameters.
  • Conducted comparative experiments to validate the ATHGS algorithm and ATHGS-GoogleNet model.

Main Results:

  • The ATHGS algorithm demonstrated superior optimization performance across three engineering designs.
  • The ATHGS-GoogleNet model achieved an accuracy of 98.1%.
  • The model exhibited high sensitivity (100%) and precision (99.5%).

Conclusions:

  • The proposed ATHGS algorithm effectively optimizes neural network parameters adaptively.
  • The ATHGS-GoogleNet model offers significant improvements in accuracy, sensitivity, and precision.
  • This approach provides an efficient solution for complex neural network parameter tuning.