Abstract or Keywords
Pong was a popular table tennis video game originally released in 1972. Pong is often used as the test subject for neural networks and genetic algorithms, often in tandem. Simple games like Pong have been optimized utilizing neural networks and genetic algorithms in order to scale difficulty or to self-sufficiently "beat" the game or human opponent [3]. However, this work attempts to scale difficulty not through behavior but through geometric morphological phenotypes. We have employed an evolutionary algorithm that weighs the performance of various Pong paddle shapes. This is tested and verified in our Pong simulation. This evolutionary algorithm modifies vertex values to generate shapes with weighted probabilities, simulate their performance in the game, establish the fitness of those shapes, and breed the most fit individuals to produce new generations of paddle shapes.