Seq103: A Unified Neuroevolution Framework for Compact Sequence Architecture Discovery
Neuroevolution is a representative neural architecture search paradigm that evolves both network topology and weights through evolutionary algorithms. In this paper, we propose Seq103, a unified NEAT-style neuroevolution framework for compact sequence architecture discovery. Seq103 consists of a shared evolutionary backbone and an optional recurrent extension. The shared backbone includes an elementary node-and-connection representation, per-class RMSE-based evaluation, mutation-based evolution with class-wise recombination, and elitism. The optional hidden-state mechanism extends the search space with hidden-state nodes and hidden connections, enabling temporal memory when step-wise recu...
| Authors | Wenxiao Li, Yongjian Liu, Qing Xie |
|---|---|
| Primary category | cs.NE |
| Published | 3 June 2026 |
| Last updated at source | 3 June 2026 |
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