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Unsupervised Learning of Echo State Networks: A case study in Artificial Embryogeny.

Alexandre Devert 1 Nicolas Bredeche 1, 2 Marc Schoenauer 1
1 TANC - Algorithmic number theory for cryptology
Inria Saclay - Ile de France, LIX - Laboratoire d'informatique de l'École polytechnique [Palaiseau]
Abstract : Echo State Networks (ESN) have demonstrated their efficiency in supervised learning of time series: a "reservoir" of neurons provide a set of dynamical systems that can be linearly combined to match the target dynamics, using a simple quadratic optimisation algorithm to tune the few free parameters. In an unsupervised learning context, however, another optimiser is needed. In this paper, an adaptive (1+1)-Evolution Strategy as well as the state-of-the-art CMA-ES are used to optimise an ESN to tackle the "flag" problem, a classical benchmark from multi-cellular artificial embryogeny: the genotype is the cell controller of a Continuous Cellular Automata, and the phenotype, the image that corresponds to the fixed point of the resulting dynamical system, must match a given 2D pattern. This approach is able to provide excellent results with few evaluations, and favourably compares to that using the NEAT algorithm (a state-of-the-art neuro-evolution method) to evolve the cell controllers. Some characteristics of the fitness landscape of the ESN-based method are also investigated.
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Submitted on : Monday, September 24, 2007 - 5:08:04 PM
Last modification on : Thursday, July 8, 2021 - 3:47:54 AM
Long-term archiving on: : Friday, April 9, 2010 - 2:45:54 AM


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  • HAL Id : inria-00174593, version 1



Alexandre Devert, Nicolas Bredeche, Marc Schoenauer. Unsupervised Learning of Echo State Networks: A case study in Artificial Embryogeny.. Evolution Artificielle, Oct 2007, Tours, France. pp.278-290. ⟨inria-00174593⟩



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