# AED2 — Genetic Algorithm for Optimizing AED Placement

> AED2 uses a genetic algorithm to optimize the placement of Automated External Defibrillators (AEDs) at convenience stores to maximize out-of-hospital cardiac arrest (OHCA) survival rates, demonstrated using Taipei 2010 EMS data.

## Authors

- Name: Chung-Yuan Huang (黃崇源) (corresponding author)
- Title: Full Professor, Department of CSIE, Chang Gung University
- Email: gscott@mail.cgu.edu.tw
- ORCID: https://orcid.org/0000-0002-8680-6755
- Google Scholar: https://scholar.google.com/citations?user=0klfzfAAAAAJ&hl=en
- Homepage: https://canslab1.github.io/

- Name: Tzai-Hung Wen
- Affiliation: Department of Geography, National Taiwan University, Taiwan

## Software

- Name: AED2
- Language: C++, Python
- License: MIT
- GitHub: https://github.com/canslab1/AED2
- README: https://canslab1.github.io/AED2/

## Publication

Huang, C.-Y. & Wen, T.-H. (2014). Optimal Installation Locations for Automated External Defibrillators in Taipei 7-Eleven Stores: Using GIS and a Genetic Algorithm with a New Stirring Operator. Computational and Mathematical Methods in Medicine, 2014, 241435.
DOI: https://doi.org/10.1155/2014/241435

## Lab

- CANS Lab (Complex Adaptive Networks and Systems Laboratory)
- Chang Gung University, Department of CSIE
- Website: https://canslab1.github.io/