- author = "Jordan MacLachlan and Yi Mei and Fangfang Zhang and Mengjie Zhang and Jessica Signal",
- title = "Learning Emergency Medical Dispatch Policies via Genetic Programming",
- booktitle = "Proceedings of the 2023 Genetic and Evolutionary Computation Conference",
- year = "2023",
- editor = "Sara Silva and Luis Paquete and Leonardo Vanneschi and Nuno Lourenco and Ales Zamuda and Ahmed Kheiri and Arnaud Liefooghe and Bing Xue and Ying Bi and Nelishia Pillay and Irene Moser and Arthur Guijt and Jessica Catarino and Pablo Garcia-Sanchez and Leonardo Trujillo and Carla Silva and Nadarajen Veerapen",
- pages = "1409--1417",
- address = "Lisbon, Portugal",
- series = "GECCO '23",
- month = "15-19 " # jul,
- organisation = "SIGEVO",
- publisher = "Association for Computing Machinery",
- publisher_address = "New York, NY, USA",
- note = "Silver 2023 HUMIES",
- keywords = "genetic algorithms, genetic programming, dynamic optimisation, hyper-heuristic, emergency medical dispatch, simulation",
- isbn13 = "9798400701191",
-
URL = "
https://human-competitive.org/sites/default/files/maclachlan_entry_form.txt",
-
URL = "
https://human-competitive.org/sites/default/files/gphhemd_maclacjord.pdf",
-
DOI = "
doi:10.1145/3583131.3590434",
- size = "9 pages",
- abstract = "Of great value to modern municipalities is the task of emergency medical response in the community. Resource allocation is vital to ensure minimal response times, which we may perform via human experts or automate by maximising ambulance coverage. To combat black-box modelling, we propose a modularised Genetic Programming Hyper Heuristic framework to learn the five key decisions of Emergency Medical Dispatch (EMD) within a reactive decision-making process. We minimise the representational distance between our work and reality by working with our local ambulance service to design a set of heuristics approximating their current decision-making processes and a set of synthetic datasets influenced by existing patterns in practice. Through our modularised framework, we learn each decision independently to identify those most valuable to EMD and learn all five decisions simultaneously, improving performance by 69percent on the largest novel dataset. We analyse the decision-making logic behind several",
-
notes = "
GECCO-2023 A Recombination of the 32nd International Conference on Genetic Algorithms (ICGA) and the 28th Annual Genetic Programming Conference (GP)",
