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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)

Abstract

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.

Keywords

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.

Notes

Acknowledgment

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).

References

  1. Al-kheder S, Wang J, Shan J (2008) Fuzzy inference guided cellular automata urban-growth modelling using multi-temporal satellite images. Int J Geogr Inf Sci 22(11):1271–1293CrossRefGoogle Scholar
  2. Batty M, Xie Y (1994) From cells to cities. Environ PlannB Plann Des 21:s31–s48CrossRefGoogle Scholar
  3. Batty M, Xie Y, Sun Z (1999) Modelling urban dynamics through GIS-based cellular automata. Comput Environ Urban Syst 23(3):205–233CrossRefGoogle Scholar
  4. Bies RR, Muldoon MF, Pollock BG, Manuck S, Smith G, Sale ME (2006) A genetic algorithm-based, hybrid machine learning approach to model selection. J Pharmacokinet Pharmacodyn 33(2):196–221CrossRefGoogle Scholar
  5. Couclelis H (1997) From cellular automata to urban models: new principles for model development and implementation. Environ PlannB Plann Des 24(2):165–174CrossRefGoogle Scholar
  6. Espinoza F, Minsker BS, Goldberg D (2001) A self-adaptive hybrid genetic algorithm. In Spector L, Goodman E, Wu A, Langdon WB, Voigt H-M, Gen M, Sen S, Dorigo M, Pezeshk S, Garzon M, Burke E (Eds.) GECCO 2001: Proceedings of the Genetic and Evolutionary Computation Conference. Morgan Kaufmann Publishers, San FranciscoGoogle Scholar
  7. He CY, Okada N, Zhang QF, Shi PJ, Zhang JS (2006) Modelling urban expansion scenarios by coupling cellular automata model and system dynamic model in Beijing, China. Appl Geogr 26(3–4):323–345CrossRefGoogle Scholar
  8. Huang MX, Gong JH, Zhou S, Liu CB, Zhang LH (2007) Genetic algorithm-based decision tree classifier for remote sensing mapping with SPOT-5 data in the Hongshimao watershed of the loess plateau, China. Neural Comput Appl 6(6):513–517Google Scholar
  9. Kee E, Airey S, Cye W (2001) An adaptive genetic algorithm. In Spector L, Goodman E, Wu A, Langdon WB, Voigt H-M, Gen M, Sen S, Dorigo M, Pezeshk S, Garzon M, Burke E (Eds.) GECCO 2001: Proceedings of the Genetic and Evolutionary Computation Conference. Morgan Kaufmann Publishers, San Francisco pp 391–397Google Scholar
  10. Li X, Yeh AGO (2002a) Neural-network-based cellular automata for simulating multiple land use changes using GIS. Int J Geogr Inf Sci 16(4):323–343CrossRefGoogle Scholar
  11. Li X, Yeh AGO (2002b) Urban simulation using principal components analysis and cellular automata for land-use planning. Photogramm Eng Rem Sens 68(4):341–351Google Scholar
  12. Li X, Yang QS, Liu XP (2007) Genetic algorithms for determining the parameters of cellular automata in urban simulation. Sci China SerD Earth Sci 50:1857–1866CrossRefGoogle Scholar
  13. Liao YL, Wang JF, Meng B, Li XH (2010) Integration of GP and GA for mapping population distribution. Int J Geogr Inf Sci 24(1):47–67CrossRefGoogle Scholar
  14. Liu Y (2008) Modelling urban development with geographical information systems and cellular automata. CRC Press, New YorkCrossRefGoogle Scholar
  15. Liu Y, Phinn SR (2003) Modelling urban development with cellular automata incorporating fuzzy-set approaches. Comput Environ Urban Syst 27(6):637–658CrossRefGoogle Scholar
  16. Lorena LAN, Furtado JC (2001) Constructive genetic algorithm for clustering problems. Evol Comput 9(3):309–327CrossRefGoogle Scholar
  17. Schmitt LM, Nehaniv CL, Fujii RH (1998) Linear analysis of genetic algorithms. Theor Comput Sci 200(1–2):101–134CrossRefGoogle Scholar
  18. Srinivas M, Patnaik LM (1994) Adaptive probabilities of crossover and mutation in genetic algorithms. IEEE Trans Syst Man Cybern 24(4):656–667CrossRefGoogle Scholar
  19. Stevens D, Dragićević S (2007) A GIS-based irregular cellular automata model of land-use change. Environ Plann B Plan Des 34(4):708–724CrossRefGoogle Scholar
  20. Stevens D, Dragićević S, Rothley K (2007) iCity: a GIS-CA modelling tool for urban planning and decision making. Environ Model Software 22(6):761–773CrossRefGoogle Scholar
  21. White RW, Engelen G (1993) Cellular automata and fractal urban form: a cellular modelling approach to the evolution of urban land use patterns. Environ Plann A 25(8):1175–1199CrossRefGoogle Scholar
  22. Wu F (1998) Simulating urban encroachment on rural land with fuzzy-logic-controlled cellular automata in a geographical information system. J Environ Manage 53(16):293–308CrossRefGoogle Scholar
  23. Wu F (2002) Calibration of stochastic cellular automata: the application to rural-urban land conversions. Int J Geogr Inf Sci 16(8):795–818CrossRefGoogle Scholar

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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