Playing Tetris using genetic algorithms

Abstract

This project discusses the training of a one-piece Tetris playing AI using the general optimization algorithms “genetic algorithms”. The player AI is implemented with two evaluation functions (exponential and linear) optimizing aset of 10 features. This player and the genetic algorithm to train it are built using only C++11 standard library. Limited to 1000 moves, the two players resulting from the training using the exponential and linear evaluation functions had average results of 381 and 421 moves, respectively, and a respective average score of 2707 and 2874. The two methods gave good results compared to the time constrains, and in the case of this project their results are very close.