GA-Tetris Bot: Evolving a Better Tetris Gameplay Using Adaptive Evaluation Scheme

Abstract

Genetic Algorithm (GA) is employed to evolve a solution for any given tetromino sequence. In contrast to previous works in this area where an evolutionary strategy was employed to evolve weights (i.e., preferences) of predefined evaluation functions which then were used to determine players’ actions, we directly evolve the actions. Each chromosome represents a plausible gameplay strategy and its fitness is evaluated by simulating the game and rating the gameplay quality using two fitness evaluation approaches: evaluating the whole board at once and evaluating local parts of the board in which they will be expanded to the whole board as the evolution progresses. We compare the results of these two evaluation tactics and also compare the evolved gameplay with actual human gameplay.