# Reinforced Genetic Algorithm Learning for Optimizing Computation Graphs

arXiv: https://arxiv.org/abs/1905.02494 · HTML: https://mlsanity.com/paper/1905.02494/

## Key facts

- Cited by 17 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 7 May 2019.

## Built on by (most cited first, 17 of 17)

- [Scaling Up Graph Neural Networks Via Graph Coarsening](https://mlsanity.com/paper/2106.05150/) (2021)
- [Robust Scheduling with GFlowNets](https://mlsanity.com/paper/2302.05446/) (2023)
- [DreamShard: Generalizable Embedding Table Placement for Recommender Systems](https://mlsanity.com/paper/2210.02023/) (2022)
- [Automap: Towards Ergonomic Automated Parallelism for ML Models](https://mlsanity.com/paper/2112.02958/) (2021)
- [Neural Topological Ordering for Computation Graphs](https://mlsanity.com/paper/2207.05899/) (2022)
- [Pre-train and Search: Efficient Embedding Table Sharding with Pre-trained Neural Cost Models](https://mlsanity.com/paper/2305.01868/) (2023)
- [A Reinforcement Learning Environment for Automatic Code Optimization in the MLIR Compiler](https://mlsanity.com/paper/2409.11068/) (2024)
- [Collage: Seamless Integration of Deep Learning Backends with Automatic Placement](https://mlsanity.com/paper/2111.00655/) (2021)
- [Resource Allocation in Disaggregated Data Centre Systems with Reinforcement Learning](https://mlsanity.com/paper/2106.02412/) (2021)
- [AH-UGC: Adaptive and Heterogeneous-Universal Graph Coarsening](https://mlsanity.com/paper/2505.15842/) (2025)
- [A Learning Method with Gap-Aware Generation for Heterogeneous DAG Scheduling](https://mlsanity.com/paper/2603.23249/) (2026)
- [Accelerate Model Parallel Training by Using Efficient Graph Traversal Order in Device Placement](https://mlsanity.com/paper/2201.09676/) (2022)
- [RLFlow: Optimising Neural Network Subgraph Transformation with World Models](https://mlsanity.com/paper/2205.01435/) (2022)
- [Partitioning Distributed Compute Jobs with Reinforcement Learning and Graph Neural Networks](https://mlsanity.com/paper/2301.13799/) (2023)
- [Expediting Distributed DNN Training with Device Topology-Aware Graph Deployment](https://mlsanity.com/paper/2302.06126/) (2023)
- [Practical Performance Guarantees for Pipelined DNN Inference](https://mlsanity.com/paper/2311.03703/) (2023)
- [DOPPLER: Dual-Policy Learning for Device Assignment in Asynchronous Dataflow Graphs](https://mlsanity.com/paper/2505.23131/) (2025)

## Abstract (arXiv)

We present a deep reinforcement learning approach to minimizing the execution cost of neural network computation graphs in an optimizing compiler. Unlike earlier learning-based works that require training the optimizer on the same graph to be optimized, we propose a learning approach that trains an optimizer offline and then generalizes to previously unseen graphs without further training. This allows our approach to produce high-quality execution decisions on real-world TensorFlow graphs in seconds instead of hours. We consider two optimization tasks for computation graphs: minimizing running time and peak memory usage. In comparison to an extensive set of baselines, our approach achieves significant improvements over classical and other learning-based methods on these two tasks.