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Type: Conference paper
Title: The experimental study of population-based parameter optimization algorithms on rule-based ecological modelling
Author: Cao, H.
Recknagel, F.
Orr, P.
Citation: Proceedings of the 2012 IEEE Congress on Evolutionary Computation, held in Brisbane, 10-15 June, 2012: pp.1-8
Publisher: IEEE
Publisher Place: USA
Issue Date: 2012
Series/Report no.: IEEE Congress on Evolutionary Computation
ISBN: 9781467315104
Conference Name: IEEE Congress on Evolutionary Computation (2012 : Brisbane, Qld.)
Statement of
Hongqing Cao, Friedrich Recknagel, Philip T. Orr
Abstract: This study investigates six population-based algorithms for the parameter optimization (PO) within the hybrid methodology developed for modelling algal abundance by rule-based models. These PO algorithms include: (1) Hill Climbing (2) Simulated Annealing (3) Genetic Algorithm (4) Differential Evolution (5) Covariance Matrix Adaptation Evolution Strategy and (6) Estimation of Distribution Algorithm. The effectiveness of algorithms is tested on the Cylindrospermopsis abundance data from Wivenhoe Reservoir in Queensland (Australia). We provide a systematic analysis and comparison of different parameter optimization algorithms as well as the resulting predictive rule models.
Keywords: ecological modelling; evolutionary algorithm; genetic programming; parameter optimization; population-based algorithm
Rights: U.S. Government work not protected by U.S. copyright
RMID: 0020122211
DOI: 10.1109/CEC.2012.6252957
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Appears in Collections:Earth and Environmental Sciences publications
Environment Institute publications

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