2017/06/05 by Filip Matzner
Computer Science · Engineering · Neuroscience · #Advanced Memory and Neural Computing #Algorithm #Artificial intelligence #Artificial neural network #CHAOS (operating system) #Computer science #Echo (communications protocol) #Echo state network #Edge of chaos #Enhanced Data Rates for GSM Evolution #Neural Networks and Reservoir Computing #Neural dynamics and brain function #Neuroevolution #Recurrent neural network #State (computer science) #cs.NE
paper · pdf · doi:10.1145/3071178.3071292
To appear in Proceedings of the Genetic and Evolutionary Computation Conference 2017 (GECCO '17)
arxiv created 2017/06/05 · arxiv updated 2017/06/06 · openalex publication_date 2017/06/30 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/05
Echo state networks represent a special type of recurrent neural networks. Recent papers stated that the echo state networks maximize their computational performance on the transition between order and chaos, the so-called edge of chaos. This work confirms this statement in a comprehensive set of experiments. Furthermore, the echo state networks are compared to networks evolved via neuroevolution. The evolved networks outperform the echo state networks, however, the evolution consumes significant computational resources. It is demonstrated that echo state networks with local connections combine the best of both worlds, the simplicity of random echo state networks and the performance of evolved networks. Finally, it is shown that evolution tends to stay close to the ordered side of the edge of chaos.