# Assessing Architectural Similarity in Populations of Deep Neural Networks

## TL;DR

This paper investigates how measuring architectural similarity in evolving neural networks can improve the selection process, potentially leading to more efficient network architectures.

## Contribution

It introduces a method to quantify architectural similarity using cluster overlap and demonstrates its effect on maintaining higher similarity in evolutionary synthesis.

## Key findings

- Networks with architectural alignment maintain higher similarity within generations.
- Architectural similarity measurement can influence the search space of neural architectures.
- Preliminary results suggest potential for improved parent selection in evolutionary neural network synthesis.

## Abstract

Evolutionary deep intelligence has recently shown great promise for producing small, powerful deep neural network models via the synthesis of increasingly efficient architectures over successive generations. Despite recent research showing the efficacy of multi-parent evolutionary synthesis, little has been done to directly assess architectural similarity between networks during the synthesis process for improved parent network selection. In this work, we present a preliminary study into quantifying architectural similarity via the percentage overlap of architectural clusters. Results show that networks synthesized using architectural alignment (via gene tagging) maintain higher architectural similarities within each generation, potentially restricting the search space of highly efficient network architectures.