# Improving Air Mobility for Pre-Disaster Planning with Neural Network Accelerated Genetic Algorithm

arXiv: https://arxiv.org/abs/2408.00790 · HTML: https://mlsanity.com/paper/2408.00790/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 1 of its 16 references that are in the map.
- First posted on arXiv 17 Jul 2024.

## Builds on (most cited first, 1 of 1)

- [Learning-to-Dispatch: Reinforcement Learning Based Flight Planning under Emergency](https://mlsanity.com/paper/2107.04897/) (2021)

## Built on by (most cited first, 1 of 1)

- [Neurosymbolic AI for Travel Demand Prediction: Integrating Decision Tree Rules into Neural Networks](https://mlsanity.com/paper/2502.01680/) (2025)

## Abstract (arXiv)

Weather disaster related emergency operations pose a great challenge to air mobility in both aircraft and airport operations, especially when the impact is gradually approaching. We propose an optimized framework for adjusting airport operational schedules for such pre-disaster scenarios. We first, aggregate operational data from multiple airports and then determine the optimal count of evacuation flights to maximize the impacted airport's outgoing capacity without impeding regular air traffic. We then propose a novel Neural Network (NN) accelerated Genetic Algorithm(GA) for evacuation planning. Our experiments show that integration yielded comparable results but with smaller computational overhead. We find that the utilization of a NN enhances the efficiency of a GA, facilitating more rapid convergence even when operating with a reduced population size. This effectiveness persists even when the model is trained on data from airports different from those under test.