# Regularized Evolutionary Algorithm for Dynamic Neural Topology Search

## TL;DR

This paper introduces a memory-efficient regularized evolutionary algorithm for neural architecture search, capable of evolving dynamic image classifiers with a small population, achieving competitive results on digit recognition datasets.

## Contribution

A novel low-memory regularized evolutionary algorithm with custom operators for neural architecture search in image classification.

## Key findings

- Achieves competitive accuracy on MNIST, USPS, SVHN datasets.
- Uses a micro-population of only 10 individuals.
- Demonstrates effectiveness of regularized evolution in neural architecture search.

## Abstract

Designing neural networks for object recognition requires considerable architecture engineering. As a remedy, neuro-evolutionary network architecture search, which automatically searches for optimal network architectures using evolutionary algorithms, has recently become very popular. Although very effective, evolutionary algorithms rely heavily on having a large population of individuals (i.e., network architectures) and is therefore memory expensive. In this work, we propose a Regularized Evolutionary Algorithm with low memory footprint to evolve a dynamic image classifier. In details, we introduce novel custom operators that regularize the evolutionary process of a micro-population of 10 individuals. We conduct experiments on three different digits datasets (MNIST, USPS, SVHN) and show that our evolutionary method obtains competitive results with the current state-of-the-art.