# Deep Learning-Based Operators for Evolutionary Algorithms

arXiv: https://arxiv.org/abs/2407.10477 · HTML: https://mlsanity.com/paper/2407.10477/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 7 of its 29 references that are in the map.
- First posted on arXiv 15 Jul 2024.

## Builds on (most cited first, 7 of 7)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Pointer Networks](https://mlsanity.com/paper/1506.03134/) (2015)
- [SpanBERT: Improving Pre-training by Representing and Predicting Spans](https://mlsanity.com/paper/1907.10529/) (2019)
- [Neural Combinatorial Optimization with Reinforcement Learning](https://mlsanity.com/paper/1611.09940/) (2016)
- [Evolution through Large Models](https://mlsanity.com/paper/2206.08896/) (2022)
- [Evolving Context-Aware Recommender Systems With Users in Mind](https://mlsanity.com/paper/2007.15409/) (2020)
- [Deep Neural Crossover](https://mlsanity.com/paper/2403.11159/) (2024)

## Abstract (arXiv)

We present two novel domain-independent genetic operators that harness the capabilities of deep learning: a crossover operator for genetic algorithms and a mutation operator for genetic programming. Deep Neural Crossover leverages the capabilities of deep reinforcement learning and an encoder-decoder architecture to select offspring genes. BERT mutation masks multiple gp-tree nodes and then tries to replace these masks with nodes that will most likely improve the individual's fitness. We show the efficacy of both operators through experimentation.