Name
Abstract | ||
|---|---|---|
Urban green spaces play a crucial role in the creation of healthy environments in densely populated areas. Agent-based systems are commonly used to model processes such as green-space allocation. In some cases, this systems delegate their spatial assignation to optimisation techniques to find optimal solutions. However, the computational time complexity and the uncertainty linked with long-term plans limit their use. In this paper we explore an approach that makes use of a statistical model which emulates the agent-based system's behaviour based on a limited number of prior simulations to inform a Genetic Algorithm. The approach is tested on a urban growth simulation, in which the overall goal is to find policies that maximise the inhabitants' satisfaction. We find that the model-driven approximation is effective at leading the evolutionary algorithm towards optimal policies. |
Year | DOI | Venue |
|---|---|---|
2013 | 10.1007/978-3-662-44440-5_21 | AGENTS AND ARTIFICIAL INTELLIGENCE, ICAART 2013 |
Keywords | Field | DocType |
Agent-based model,Genetic algorithm,Statistical model,Optimisation,Uncertainty,Green space planning | Mathematical optimization,Agent-based model,Delegate,Computer science,Artificial intelligence,Statistical model,Time complexity,Genetic algorithm,Machine learning | Conference |
Volume | ISSN | Citations |
449 | 1865-0929 | 1 |
PageRank | References | Authors |
0.35 | 10 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Marta Vallejo | 1 | 12 | 2.96 |
| David W. Corne | 2 | 2161 | 152.00 |
| Verena Rieser | 3 | 423 | 36.46 |