{"type":"ScholarlyArticle","title":"Provably Sub-Linear Two-Timescale NeuroEvolution with Online Plasticity","authors":[{"name":"Shishen Lin","slug":"shishen-lin","given":"Shishen","family":"Lin","affiliation":""},{"name":"Yixin Chen","slug":"yixin-chen","given":"Yixin","family":"Chen","affiliation":""}],"abstract":"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 inner loop for online weight adaptation via reward-modulated plasticity. Under mild conditions, we prove that NEOL achieves sublinear regret. Empirically, under fixed interaction budgets on four standard control benchmarks, a NEAT-based NEOL implementation achieves higher final fitness and lower variance than pure NEAT, and is competitive with strong reinforcement learning (RL) baselines on several tasks. The results are supported by Wilcoxon rank-sum tests and ablation studies. Overall, the findings show that online plasticity can improve the sample efficiency and robustness of two-timescale neuroevolution. Code is available at https://github.com/boobaa2001/NeuroEvolution_Online_Learning_NEOL","doi":"10.24963/ijcai.2026/703","url":"https://ijcai.org/proceedings/2026/703","pdf":"https://ijcai.org/proceedings/2026/0703.pdf","markdown":"https://ijcai.org/proceedings/2026/703.md","bibtex":"https://ijcai.org/proceedings/2026/bibtex/703","volume":{"slug":"2026","title":"Proceedings of the Thirty-Fifth International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2026","url":"https://ijcai.org/proceedings/2026/","year":2026,"location":"Bremen, Germany","dates":"15-21 August 2026","editors":"Diego Calvanese","isbn":"978-1-956792-09-6","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2026"},"section":"Search","track":"Main Track","number":703,"pages":{"first":6317,"last":6325},"keywords":["Search: Meta-reasoning and meta-heuristics","Search: Evolutionary computation","Search: Local search","Search: Search and machine learning","Search: Heuristic search"],"videos":[],"references":[],"license_note":"Copyright International Joint Conferences on Artificial Intelligence."}