@article{ART003374556},
author={MyounJae Lee},
title={Analysis of Autonomous Exploration Performance and Convergence of Game NPCs Based on Genetic Algorithm Parameter Control},
journal={Journal of Internet of Things and Convergence},
issn={2466-0078},
year={2026},
volume={12},
number={4},
pages={15}
TY - JOUR
AU - MyounJae Lee
TI - Analysis of Autonomous Exploration Performance and Convergence of Game NPCs Based on Genetic Algorithm Parameter Control
JO - Journal of Internet of Things and Convergence
PY - 2026
VL - 12
IS - 4
PB - The Korea Internet of Things Society
SP - 15
EP -
SN - 2466-0078
AB - Genetic Algorithm (GA) is a machine learning technique that searches for optimal solutions by imitating biological natural selection and evolutionary principles, and is widely utilized in complex optimization problems and AI-based pathfinding. This study maximizes the pathfinding performance of non-player characters (NPCs) in games by applying this algorithm, and investigates its convergence process and evolutionary characteristics through various scenarios. To this end, a fitness function evaluating the distance to the target point, individual survival time, and final arrival status was constructed in the Unity engine environment, and simulations of 100 generations per scenario were repeated 30 times in total. The experimental results showed that as generations progressed, the average and minimum distances to the destination decreased, showing a stable convergence trend, and the number of individuals successfully reaching the target steadily increased. Through this, the autonomous learning effect of the genetic algorithm in complex terrains was demonstrated, and the possibility of developing into an adaptive model applying dynamic parameter adjustment techniques in the future was confirmed.
KW - Genetic Algorithm (GA);Path Optimization;Generational Evolution;Convergence Analysis Fitness Function
DO -
UR -
ER -
MyounJae Lee. (2026). Analysis of Autonomous Exploration Performance and Convergence of Game NPCs Based on Genetic Algorithm Parameter Control. Journal of Internet of Things and Convergence, 12(4), 15.
MyounJae Lee. 2026, "Analysis of Autonomous Exploration Performance and Convergence of Game NPCs Based on Genetic Algorithm Parameter Control", Journal of Internet of Things and Convergence, vol.12, no.4 15.
MyounJae Lee "Analysis of Autonomous Exploration Performance and Convergence of Game NPCs Based on Genetic Algorithm Parameter Control" Journal of Internet of Things and Convergence 12.4 15 (2026) : 15.
MyounJae Lee. Analysis of Autonomous Exploration Performance and Convergence of Game NPCs Based on Genetic Algorithm Parameter Control. 2026; 12(4), 15.
MyounJae Lee. "Analysis of Autonomous Exploration Performance and Convergence of Game NPCs Based on Genetic Algorithm Parameter Control" Journal of Internet of Things and Convergence 12, no.4 (2026) : 15.
MyounJae Lee. Analysis of Autonomous Exploration Performance and Convergence of Game NPCs Based on Genetic Algorithm Parameter Control. Journal of Internet of Things and Convergence, 12(4), 15.
MyounJae Lee. Analysis of Autonomous Exploration Performance and Convergence of Game NPCs Based on Genetic Algorithm Parameter Control. Journal of Internet of Things and Convergence. 2026; 12(4) 15.
MyounJae Lee. Analysis of Autonomous Exploration Performance and Convergence of Game NPCs Based on Genetic Algorithm Parameter Control. 2026; 12(4), 15.
MyounJae Lee. "Analysis of Autonomous Exploration Performance and Convergence of Game NPCs Based on Genetic Algorithm Parameter Control" Journal of Internet of Things and Convergence 12, no.4 (2026) : 15.
