Evolution of Cartesian Genetic Programs for Development of Learning Neural Architecture

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Born to Learn: the Inspiration, Progress, and Future of Evolved Plastic Artificial Neural Networks (2017, background): “In Khan et al. (2011); Khan and Miller (2014), the authors introduce a large number of bio-inspired mechanisms to evolve networks with rich learning dynamics.”
Evolving Plastic Neural Networks for Online Learning: Review and Future Directions (2012, background): “Khan and Miller [15] introduced a modified version of Wumpus world, where pits and the Wumpus only harm rather than kill the agent, as well as a competitive version of Wumpus world where the Wumpus acts as a predator.”
Neuro-Centric and Holocentric Approaches to the Evolution of Developmental Neural Networks (2014, methodology): “The weights of axon and dendrite branches are also used to modulate and transfer the simulated potential [28].”
In search of intelligence: evolving a developmental neuron capable of learning (2014, methodology): “We have found the evidence from neuroscience, on the importance of time-dependent morphological processes in learning, so compelling, that we have included it in a model of an artiﬁcial developmental neuron (Miller & Khan, 2011).”