Provably Sub-Linear Two-Timescale NeuroEvolution with Online Plasticity
NeuroEvolution of Augmenting Topologies (NEAT) is a widely used neuroevolution algorithm for learning neural network architectures and weights for control tasks. However, standard offline optimisation searches for connection strengths directly, which can scale poorly in high-dimensional weight spaces and more difficult continuous control problems. Hybrid methods that combine neuroevolution with online learning can address this challenge, but their theoretical properties remain underexplored. This paper gives the first regret analysis for a general NeuroEvolutionary Online Learning (NEOL) framework, which decouples learning into two timescales: an outer loop for architecture search and an ...
| Authors | Shishen Lin, Yixin Chen |
|---|---|
| Primary category | cs.NE |
| Published | 18 June 2026 |
| Last updated at source | 18 June 2026 |
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