A Hypercube-Based Encoding for Evolving Large-Scale Neural Networks | Semantic Scholar

This article demonstrates a neuroevolution system, Enforced Sub-populations (ESP), that is used to evolve a controller for the standard double pole task and a much harder, non-Markovian version, and introduces an incremental method that evolves on a sequence of tasks, and utilizes a local search technique (Delta-Coding) to sustain diversity.

Results produced with CPPNs through interactive evolution of two-dimensional images show that such an encoding can nevertheless produce structural motifs often attributed to more conventional developmental abstractions, suggesting that local interaction may not be essential to the desirable properties of natural encoding in the way that is usually assumed.

This taxonomy provides a unified context for long-term research in AE, so that implementation decisions can be compared and contrasted along known dimensions in the design space of embryogenic systems, and allows predicting how the settings of various AE parameters affect the capacity to efficiently evolve complex phenotypes.

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A new method is described in which the degrees of freedom of the representation are increased incrementally, creating genotype-phenotype maps that are exquisitely tuned to the specifics of the epistatic fitness function, creating adaptive landscapes that are much smoother than generic NK landscapes with the same genotypes.

The New AI hypothesizes that intelligent behaviour must be understood within the framework provided by the agent’s physical interactions with the environment: subjective sensations and bodily interactions, and proposes a bottom-up exploration, which starts from the lowest adaptive mechanisms to reach the topmost cognitive abilities.