An evolutionary race: A comparison of genetic algorithms and particle swarm optimization used for training neural networks
This paper compares the performance of genetic algorithms (GA) and particle swarm optimization (PSO) when used to train artificial neural networks. The networks are used to control virtual racecars, with the aim of successfully navigating around a track in the shortest possible period of time. Each car is mounted with multiple straight-line distance sensors, which provide the input to the networks. The cars act as autonomous agents for the duration of the training run: they record the distance traveled and rely on this for fitness evaluations. Both evolutionary algorithms are well suited to this unsupervised learning task, and the networks learn to successfully navigate the course in a minimal number of generations. The paper shows that PSO is superior for this application: it trains networks faster and more accurately than GAs do, once properly optimized.
|Conference||Proceedings of the International Conference on Artificial Intelligence, IC-AI'04|
Clow, B. (Brian), & White, A. (2004). An evolutionary race: A comparison of genetic algorithms and particle swarm optimization used for training neural networks. In Proceedings of the International Conference on Artificial Intelligence, IC-AI'04 (pp. 582–588).