Combining Procedural Generation and Genetic Algorithms to Model Urban Growth

Etienne Tack, Etienne Tack, Gilles Énée, Frédéric Flouvat

2025

Abstract

In this paper, we present an approach to model spatial influences in multi-agent models using procedural generation and genetic algorithms. We applied this approach in an urban growth model. In agent-based simulations, the agents make decisions based on the perception of their environment. In our context, the agents represent inhabitants who can create new buildings or extend the existing ones. Their behaviour is ruled by spatial influences (e.g., the proximity of the road increases chances of building in the surrounding areas). Procedural generation provides a good framework for representing the influences of the environment on the agent’s behaviour. Each spatial feature is associated with an influence function. Their parameters search space can be tremendous, making it difficult for field experts to set them manually. Consequently, we use a genetic algorithm to optimize the parameters of these influence functions and train the model based on three spatial measures (Chamfer distance, kernel density, and a density grid). This approach can be employed likewise to any problem where the agent decisions are wholly or partly based on location. Our experiments highlight the interest of our approach and the impact of the chosen fitness functions.

Paper Citation

in Harvard Style

Tack E., Énée G. and Flouvat F. (2025). Combining Procedural Generation and Genetic Algorithms to Model Urban Growth. In Proceedings of the 17th International Conference on Agents and Artificial Intelligence - Volume 1: ICAART; ISBN 978-989-758-737-5, SciTePress, pages 420-428. DOI: 10.5220/0013165200003890

in Bibtex Style

@conference{icaart25,
author={Etienne Tack and Gilles Énée and Frédéric Flouvat},
title={Combining Procedural Generation and Genetic Algorithms to Model Urban Growth},
booktitle={Proceedings of the 17th International Conference on Agents and Artificial Intelligence - Volume 1: ICAART},
year={2025},
pages={420-428},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0013165200003890},
isbn={978-989-758-737-5},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 17th International Conference on Agents and Artificial Intelligence - Volume 1: ICAART
TI - Combining Procedural Generation and Genetic Algorithms to Model Urban Growth
SN - 978-989-758-737-5
AU - Tack E.
AU - Énée G.
AU - Flouvat F.
PY - 2025
SP - 420
EP - 428
DO - 10.5220/0013165200003890
PB - SciTePress