Seq103: New Neuroevolution Framework Achieves Compact Sequence Models with Minimal Parameter Loss
Researchers introduced Seq103, a neuroevolution framework that automatically discovers compact neural network architectures for sequence classification tasks. The framework uses evolutionary algorithms to optimize both network topology and weights, with an optional recurrent extension for temporal memory. Seq103 maintains 82-87% of baseline accuracy while reducing model parameters by orders of magnitude, relevant for deploying AI on resource-constrained devices.
Seq103 is a unified NEAT-style neuroevolution framework designed to discover efficient sequence classification architectures through evolutionary search. The system features a shared evolutionary backbone with node-and-connection representation, class-wise mutation and recombination, and an optional hidden-state mechanism that enables temporal memory for recurrent tasks. Evaluated on 8 text classification datasets and 128 univariate time-series datasets from UCRArchive2018, Seq103 achieved 86.96% of baseline accuracy on step-wise recurrent tasks while using 34.6x to 3218.0x fewer parameters, and 81.95% accuracy on sample-wise feedforward tasks with 11.8x to 160,601.0x parameter reduction. The framework's unified design allows the same core search pipeline to handle both recurrent and feedforward sequence problems by toggling the hidden-state extension on or off.
What's missing
The paper does not discuss computational cost of the evolutionary search process itself, wall-clock training time comparisons with baseline methods, or how search time scales with dataset size. Additionally, the study does not address how Seq103 performs on longer sequences or whether the discovered architectures generalize to out-of-distribution data.
What different sources said
- arXiv cs.AICenter
Seq103: A Unified Neuroevolution Framework for Compact Sequence Architecture Discovery
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