# Population-based guiding for evolutionary neural architecture search

## TL;DR

This paper introduces a new method for efficiently searching neural network architectures using evolutionary techniques guided by population data.

## Contribution

The novel PBG framework combines greedy selection and guided mutation to improve the efficiency of evolutionary NAS.

## Key findings

- PBG outperforms baseline methods like regularized evolution by up to three times on NAS-Bench-101.
- The framework effectively balances exploration and exploitation in neural architecture search.
- PBG achieves competitive performance across multiple NAS benchmarks.

## Abstract

Neural Architecture Search (NAS)—combined with biology-inspired evolutionary methods—can help discover suitable architectures tailored to a given objective. A guided evolutionary approach can enhance efficiency, aiming to accelerate the discovery of top-performing architectures within a given search space. We propose a novel algorithmic framework that implements selection, crossover, and mutation operations to generate new candidate architectures during an evolutionary Neural Architecture Search: A greedy selection operator, relying solely on model accuracy data, promotes exploitation. Incorporating architecture embeddings to further refine the mutation process enhances exploration. We introduce a guided mutation approach to steer the search toward unexplored regions of the current population. The proposed strategy, PBG (Population-Based Guiding), synergizes both explorative and exploitative methods. It substantially outperforms baseline methods such as regularized evolution by being up to three times faster on NAS-Bench-101. This combined approach not only leverages the strengths of both explorative guided mutation and exploitative greedy selection strategies, but also provides a robust and efficient framework reaching competitive performance for evolutionary Neural Architecture Search across benchmarks.