Supervised and Evolutionary Learning of Echo State Networks

Fei Jiang 1, 2 Hugues Berry 1 Marc Schoenauer 2
1 ALCHEMY - Architectures, Languages and Compilers to Harness the End of Moore Years
LRI - Laboratoire de Recherche en Informatique, UP11 - Université Paris-Sud - Paris 11, CNRS - Centre National de la Recherche Scientifique : UMR8623, Inria Saclay - Ile de France
2 TAO - Machine Learning and Optimisation
LRI - Laboratoire de Recherche en Informatique, UP11 - Université Paris-Sud - Paris 11, Inria Saclay - Ile de France, CNRS - Centre National de la Recherche Scientifique : UMR8623
Abstract : A possible alternative to topology fine-tuning for Neural Net- work (NN) optimization is to use Echo State Networks (ESNs), recurrent NNs built upon a large reservoir of sparsely randomly connected neurons. The promises of ESNs have been fulfilled for supervised learning tasks, but unsupervised ones, e.g. control problems, require more flexible optimization methods – such as Evolutionary Algorithms. This paper proposes to apply CMA-ES, the state-of-the-art method in evolutionary continuous parameter optimization, to the evolutionary learning of ESN parameters. First, a standard supervised learning problem is used to validate the approach and compare it to the standard one. But the flexibility of Evolutionary optimization allows us to optimize not only the outgoing weights but also, or alternatively, other ESN parameters, sometimes leading to improved results. The classical double pole balancing control problem is then used to demonstrate the feasibility of evolutionary (i.e. reinforcement) learning of ESNs. We show that the evolutionary ESN obtain results that are comparable with those of the best topology-learning methods.
Document type :
Conference papers
Complete list of metadatas

Cited literature [16 references]  Display  Hide  Download

https://hal.inria.fr/inria-00337235
Contributor : Fei Jiang <>
Submitted on : Thursday, November 6, 2008 - 2:46:18 PM
Last modification on : Monday, December 9, 2019 - 5:24:06 PM
Long-term archiving on: Tuesday, October 9, 2012 - 3:06:40 PM

File

PPSN08-Jiang.F-Berry.H-Schoena...
Files produced by the author(s)

Identifiers

  • HAL Id : inria-00337235, version 1

Collections

Citation

Fei Jiang, Hugues Berry, Marc Schoenauer. Supervised and Evolutionary Learning of Echo State Networks. International Conference on Parallel Problem Solving From Nature, Sep 2008, Dortmund, Germany. ⟨inria-00337235⟩

Share

Metrics

Record views

324

Files downloads

1337