TY  - JOUR
T1  - Heuristic Optimization of a Tetris Bot Using Genetic Algorithms: An Adaptive Evolutionary Approach
AU  - Erkalkan, Ercan
PY  - 2026
DA  - February
Y2  - 2025
DO  - 10.47000/tjmcs.1663275
JF  - Turkish Journal of Mathematics and Computer Science
JO  - TJMCS
PB  - Matematikçiler Derneği
WT  - DergiPark
SN  - 2148-1830
SP  - 220
EP  - 247
VL  - 18
IS  - 1
LA  - en
AB  - This study presents a Genetic Algorithm (GA)-driven optimization framework for enhancing the heuristic evaluation function of a Tetris bot. The proposed approach combines offline evolutionary training with real-time dynamic weight adjustments to adapt the bot's strategy to evolving gameplay conditions. Key heuristic features—including hole minimization, line clearing, and surface bumpiness—are weighted dynamically based on board state metrics such as maximum column height. Evaluated through 100 independent simulations, the GA-optimized bot demonstrated significant performance improvements over a baseline bot with fixed heuristic weights: +61.79\% in average lines cleared (91.38 vs. 56.48) and +55.74\% in average game duration (3.17 vs. 2.04 minutes). While decision latency increased marginally (7.33 ms vs. 7.05 ms), this trade-off was justified by the bot's enhanced strategic adaptability, evidenced by reduced performance variance and outlier frequency. The results validate GA's efficacy in optimizing complex, multi-objective decision-making processes in dynamic environments. Future work will explore hybrid GA-reinforcement learning architectures and applications to other real-time strategy games.
KW  - tetris bot
KW  - genetic algorithms
KW  - heuristic optimization
KW  - dynamic weighting
KW  - evolutionary computation
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UR  - https://doi.org/10.47000/tjmcs.1663275
L1  - https://dergipark.org.tr/en/download/article-file/4714844
ER  -