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Related Experiment Video

Updated: Jan 12, 2026

Intraventricular Transplantation of Engineered Neuronal Precursors for In Vivo Neuroarchitecture Studies

Intraventricular Transplantation of Engineered Neuronal Precursors for In Vivo Neuroarchitecture Studies

Published on: May 11, 2019

Population-based guiding for evolutionary neural architecture search.

Stefan Dendorfer¹, Andreas M Kist²

¹Department Artificial Intelligence in Biomedical Engineering, Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Germany.

Scientific Reports

|November 7, 2025

Summary

This summary is machine-generated.

This study introduces Population-Based Guiding (PBG), a novel framework for evolutionary Neural Architecture Search (NAS). PBG accelerates the discovery of high-performing neural network architectures by combining guided mutation and greedy selection.

Keywords:

Benchmarks Guided evolution Neural architecture search

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Last Updated: Jan 12, 2026

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Published on: May 12, 2019

Area of Science:

Artificial Intelligence
Machine Learning
Computer Science

Background:

Neural Architecture Search (NAS) combined with evolutionary methods aids in discovering optimal neural network architectures.
Guided evolutionary approaches can improve the efficiency of NAS.
Existing methods may lack a balance between exploration and exploitation.

Purpose of the Study:

To develop a novel algorithmic framework for evolutionary Neural Architecture Search (NAS).
To enhance the efficiency and performance of NAS by synergizing explorative and exploitative strategies.
To accelerate the discovery of top-performing architectures within a defined search space.

Main Methods:

Implemented a novel framework, Population-Based Guiding (PBG), for evolutionary NAS.
Utilized greedy selection based on accuracy for exploitation.
Incorporated architecture embeddings and guided mutation for enhanced exploration.
Combined explorative and exploitative methods within a unified strategy.

Main Results:

PBG substantially outperforms baseline methods like regularized evolution.
The proposed strategy demonstrated up to three times faster performance on NAS-Bench-101.
Achieved competitive performance across benchmarks for evolutionary NAS.

Conclusions:

PBG offers a robust and efficient framework for evolutionary NAS.
The synergy of guided mutation and greedy selection enhances both exploration and exploitation.
The approach effectively accelerates the discovery of high-performing neural network architectures.