An Optimised Cellular Automata Model Based on Adaptive Genetic Algorithm for Urban Growth Simulation

  • Yongjiu Feng
  • Yan Liu
Conference paper
Part of the Lecture Notes in Geoinformation and Cartography book series (LNGC)


This paper presents an improved cellular automata (CA) model optimised using an adaptive genetic algorithm (AGA) to simulate the spatio-temporal processes of urban growth. The AGA technique was used to optimise the transition rules of the CA model defined through conventional logistic regression approach, resulting in higher simulation efficiency and improved results. Application of the AGA based CA model in Shanghais Jiading District, Eastern China demonstrates that the model was able to generate reasonable representation of urban growth even with limited input data in defining its transition rules. The research shows that AGA technique can be integrated within a conventional CA based urban simulation model to improve human understanding on urban dynamics.


Cellular Automaton Cellular Automaton Urban Growth Transition Rule Urban State 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.



This study was supported by the Innovation Program of Shanghai Municipal Education Commission (project no. 11YZ154), the Special Research Fund for Selected Outstanding Young University Scholars in Shanghai (project no. SSC09018), and the University of Queensland New Staff Research Start-up Fund (project no. 601871).


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Copyright information

© Springer-Verlag GmbH Berlin Heidelberg 2012

Authors and Affiliations

  1. 1.College of Marine SciencesShanghai Ocean UniversityShanghaiPeople’s Republic of China
  2. 2.Centre for Spatial Environmental Research and School of Geography, Planning and Environmental ManagementThe University of Queensland, AustraliaSt LuciaAustralia

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