Overcoming deception in evolution of cognitive behaviors

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How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

Born to Learn: the Inspiration, Progress, and Future of Evolved Plastic Artificial Neural Networks (2017, methodology): “A focus on the deceptiveness of evolving to learn is presented in Risi et al. (2010); Lehman and Miikkulainen (2014).”
Evolving Deep LSTM-based Memory Networks using an Information Maximization Objective (2016, methodology): “T-Maze has been widely used as a benchmark problem for evaluating agents with memory [3, 5, 15, 18].”
Evolving Neuromodulatory Topologies for Plasticity in Video Game Playing (2016, background): “It has potential to be combined with other life-long learning methods such as hypothesis testing in distal reward learning [22], larger indirectly encoded networks [19], [16], overcoming deception in evolution of cognitive behaviors [12], and learning big collections of different behavior [3].”
Evolving Neural Turing Machines for Reward-based Learning (2016, methodology): “Especially tasks involving cognitive behaviors and adaptation have shown to be very deceptive for objective-based search methods [7, 9, 12] and benefit from more explorative search methods such as novelty search [8].”
Discovering Gated Recurrent Neural Network Architectures (2018, methodology): “Recent work in this area suggests that optimizing the network architecture using a meta-learning algorithm like Bayesian optimization (Malkomes et al. (2015)), reinforcement learning (Zoph and Le (2016) Baker et al.”