Evolving optimal spatial allocation policies for complex and uncertain environments

Research output: Chapter in Book/Report/Conference proceedingConference contribution

2 Citations (Scopus)

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.

Original languageEnglish
Title of host publicationCommunications in Computer and Information Science
PublisherSpringer
Pages351-369
Number of pages19
Volume449
ISBN (Print)9783662444399
DOIs
Publication statusPublished - 1 Jan 2014
Event5th International Conference on Agents and Artificial Intelligence - Barcelona, United Kingdom
Duration: 15 Feb 201318 Feb 2013

Publication series

NameCommunications in Computer and Information Science
Volume449
ISSN (Print)18650929

Conference

Conference5th International Conference on Agents and Artificial Intelligence
Abbreviated titleICAART 2013
CountryUnited Kingdom
CityBarcelona
Period15/02/1318/02/13

Keywords

  • Agent-based model
  • Genetic algorithm
  • Green space planning
  • Optimisation
  • Statistical model
  • Uncertainty

ASJC Scopus subject areas

  • Computer Science(all)

Fingerprint

Dive into the research topics of 'Evolving optimal spatial allocation policies for complex and uncertain environments'. Together they form a unique fingerprint.

Cite this