TY - JOUR
T1 - A hybrid genetic algorithm with local optimiser improves calibration of a vegetation change cellular automata model
AU - Whitsed, Rachel
AU - Smallbone, Lisa T.
N1 - Includes bibliographical references.
PY - 2017
Y1 - 2017
N2 - Cellular automata (CA) models are commonly used to model vegetation dynamics, with the genetic algorithm (GA) being one method of calibration. This article investigates different GA settings, as well as the combination of a GA with a local optimiser to improve the calibration effort. The case study is a pattern-calibrated CA to model vegetation regrowth in central Victoria, Australia. We tested 16 GA models, varying population size, mutation rate, and level of allowable mutation. We also investigated the effect of applying a local optimiser, the Nelder‒Mead Downhill Simplex (NMDS) at GA convergence. We found that using a decreasing mutation rate can reduce computational cost while avoiding premature GA convergence, while increasing population size does not make the GA more efficient. The hybrid GA-NMDS can also reduce computational cost compared to a GA alone, while also improving the calibration metric. We conclude that careful consideration of GA settings, including population size and mutation rate, and in particular the addition of a local optimiser, can positively impact the efficiency and success of the GA algorithm, which can in turn lead to improved simulations using a well-calibrated CA model.
AB - Cellular automata (CA) models are commonly used to model vegetation dynamics, with the genetic algorithm (GA) being one method of calibration. This article investigates different GA settings, as well as the combination of a GA with a local optimiser to improve the calibration effort. The case study is a pattern-calibrated CA to model vegetation regrowth in central Victoria, Australia. We tested 16 GA models, varying population size, mutation rate, and level of allowable mutation. We also investigated the effect of applying a local optimiser, the Nelder‒Mead Downhill Simplex (NMDS) at GA convergence. We found that using a decreasing mutation rate can reduce computational cost while avoiding premature GA convergence, while increasing population size does not make the GA more efficient. The hybrid GA-NMDS can also reduce computational cost compared to a GA alone, while also improving the calibration metric. We conclude that careful consideration of GA settings, including population size and mutation rate, and in particular the addition of a local optimiser, can positively impact the efficiency and success of the GA algorithm, which can in turn lead to improved simulations using a well-calibrated CA model.
KW - Calibration
KW - Cellular automata
KW - Genetic algorithm
KW - Nelder‒Mead Downhill Simplex
KW - Vegetation regrowth
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U2 - 10.1080/13658816.2016.1231315
DO - 10.1080/13658816.2016.1231315
M3 - Article
AN - SCOPUS:84987648001
VL - 31
SP - 717
EP - 737
JO - International Journal of Geographical Information Systems
JF - International Journal of Geographical Information Systems
SN - 1365-8816
IS - 4
ER -