他在香港非政府组织’23, Can Tho City, Vietnam

Majors: Computer Science; Math

Neuroevolution is a set of algorithms that use evolutionary algorithms to optimize neural networks without much domain knowledge. We analyze Neuroevolution of Augmented Topologies (NEAT) and its extension HyperNeat in this paper. NEAT evolves both the topology and weight values of a network along with novel ideas of applying speciation, tracking genes, and evolving from simple structures. HyperNEAT uses similar techniques to evolve networks but instead of using direct graph encoding as in NEAT, it uses indirect graph encoding. We use a stochastic single-player game, 2048, as the benchmark problem to compare two algorithms’ performance. Even though the game is simple, it has the random factor that may pose a challenge in finding a strategy to achieve a high score. The paper analyzes the strategy and the performance of NEAT and HyperNEAT in 2048 with different parameter settings. Furthermore, code and future work are specified at the end of the paper.