# Deep Learning with Darwin: Evolutionary Synthesis of Deep Neural Networks

arXiv: https://arxiv.org/abs/1606.04393 · HTML: https://mlsanity.com/paper/1606.04393/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 14 Jun 2016.

## Built on by (most cited first, 3 of 3)

- [Knowledge Fusion By Evolving Weights of Language Models](https://mlsanity.com/paper/2406.12208/) (2024)
- [AttendSeg: A Tiny Attention Condenser Neural Network for Semantic Segmentation on the Edge](https://mlsanity.com/paper/2104.14623/) (2021)
- [Rethinking Pareto Frontier for Performance Evaluation of Deep Neural Networks](https://mlsanity.com/paper/2202.09275/) (2022)

## Abstract (arXiv)

Taking inspiration from biological evolution, we explore the idea of "Can deep neural networks evolve naturally over successive generations into highly efficient deep neural networks?" by introducing the notion of synthesizing new highly efficient, yet powerful deep neural networks over successive generations via an evolutionary process from ancestor deep neural networks. The architectural traits of ancestor deep neural networks are encoded using synaptic probability models, which can be viewed as the `DNA' of these networks. New descendant networks with differing network architectures are synthesized based on these synaptic probability models from the ancestor networks and computational environmental factor models, in a random manner to mimic heredity, natural selection, and random mutation. These offspring networks are then trained into fully functional networks, like one would train a newborn, and have more efficient, more diverse network architectures than their ancestor networks, while achieving powerful modeling capabilities. Experimental results for the task of visual saliency demonstrated that the synthesized `evolved' offspring networks can achieve state-of-the-art performance while having network architectures that are significantly more efficient (with a staggering ~48-fold decrease in synapses by the fourth generation) compared to the original ancestor network.