Developmental Plasticity in Cartesian Genetic Programming based Neural Networks

By Maryam Mahsal Khan, Gul Muhammad Khan, Julian Francis Miller. 2016

4 citations recorded by the source corpus.

This work presents a method for exploiting developmental plasticity in Artificial Neural Networks using Cartesian Genetic Programming. This is inspired by developmental plasticity that exists in the biological brain allowing it to adapt to a changing environment. The network architecture used is that of a static Cartesian Genetic Programming ANN, which has recently been introduced. The network is plastic in terms of its dynamic architecture, connectivity, weights and functionality that can change in response to the environmental signals. The dynamic capabilities of the algorithm are tested on a standard benchmark linear/non-linear control problems (i.e. pole-balancing).

Subfields: Industrial and Manufacturing Engineering

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Born to Learn: the Inspiration, Progress, and Future of Evolved Plastic Artificial Neural Networks (2017, background): “ut was proposed in Schmidhuber et al. (2007), showing that the evolved RNNs were more compact and resulted in better learning than randomly initialized echo state networks (Jaeger and Haas, 2004). In Khan et al. (2011b,a); Khan and Miller (2014), the authors introduced a large number of bio-inspired”