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arXiv Machine Learning By Lishan Yu, Derek Jiu, Qizhen Lan, Xiaoqian Jiang

EvoTreeNAD: Genealogy-Guided Evolution for LLM-Driven Neural Architecture Discovery

Read the original on arXiv Machine Learning →

EvoTreeNAD is a genealogy‑guided evolutionary algorithm that autonomously discovers neural architectures without a predefined seed or search space. Starting from an empty root, it builds a persistent genealogy where each node represents a complete architecture; top‑percentile values from nodes and descendants steer lineage selection. The method combines an Idea Agent that proposes variants and a Code Agent that implements them, with theoretical analysis showing stationary variation regimes and empirical results demonstrating superior performance on CIFAR‑10/100 and MedMNIST‑v2 tasks.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

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