Evolving neural networks

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World Models (2018, methodology): “Before the popularity of Deep RL methods (Mnih et al., 2013), evolution-based algorithms have been shown to be effective at solving RL tasks (Stanley & Miikkulainen, 2002; Gomez et al., 2008; Gomez & Schmidhuber, 2005; Gauci & Stanley, 2010; Sehnke et al., 2010; Miikkulainen, 2013).”
Metaheuristic design of feedforward neural networks: A review of two decades of research (2017, methodology): “Therefore, EP and GP can be directly applied to a population FNN architecture to evolve an optimum FNN architecture [148,214,215].”
An efficient automated parameter tuning framework for spiking neural networks (2014, background): “Although EAs are an effective tool for constructing ANNs, they often require long execution times to produce well-tuned networks.”
Autonomous Evolution of Topographic Regularities in Artificial Neural Networks (2010, background): “For at least the last 25 years (Fogel et al., 1990; Harp et al., 1989; Kampfner and Conrad, 1983), a unique and growing research community has focused on the possibility that effective artificial neural networks (ANNs) can be evolved through evolutionary algorithms (Angeline et al.”