Snake Game: A genetic neural network approach

snake game genetic algorithm neural network parameter tuning

Authors

  • Shen Hau Hor School of Computing Asia Pacific University of Technology & Innovation (APU) Kuala Lumpur, Malaysia
  • Mun Kye Yan School of Computing Asia Pacific University of Technology & Innovation (APU) Kuala Lumpur, Malaysia
  • Yoke Shin Sim School of Computing Asia Pacific University of Technology & Innovation (APU) Kuala Lumpur, Malaysia
  • Sheng Jeh Tan School of Computing Asia Pacific University of Technology & Innovation (APU) Kuala Lumpur, Malaysia
  • Zailan Arabee bin Abdul Salam
    zailan@apu.edu.my
    School of Computing Asia Pacific University of Technology and Innovation (APU) Kuala Lumpur, Malaysia
Vol. 6 No. 1 (2022)
Original Research
January 16, 2026

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There have been multiple attempts at playing the snake game with AI approaches which include Genetic Algorithm and Neural Network. This paper aims to research how tuning several parameters of the genetic algorithm and neural network will affect a snake agent in its performance. The parameters changed in this experiment are the mutation percent, percentage of best/worst performing, mutation intensity, and arena size to test the effects of each parameter on the performance of the snake agent. The consistency as well as performance of the snake agent are both observed closely in this study. We have found out that each parameter has its own degree of effect on the performance of the snake agent.