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
Country/TerritoryUnited 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)

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