Preliminary investigations on the evolvability of a non-spatial GasNet model

Authors:
Patricia A. Vargas;Ezequiel A. Di Paolo;Phil Husbands
Affiliations:
Centre for Computational Neuroscience and Robotics, University of Sussex, United Kingdom;Centre for Computational Neuroscience and Robotics, University of Sussex, United Kingdom;Centre for Computational Neuroscience and Robotics, University of Sussex, United Kingdom
Venue:
ECAL'07 Proceedings of the 9th European conference on Advances in artificial life
Year:
2007

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Abstract

This paper addresses the role of space in evolving a novel Non-Spatial GasNet model. It illustrates that this particular neural network model which make use of modulatory effects of diffusing gases has its evolvability improved when its neurons are not constrained to a Euclidean space. The results show that successful behaviour is achieved in fewer evaluations for the novel unconstrained GasNet than for the original model.