NDLI: The experimental study of population-based parameter optimization algorithms on rule-based ecological modelling

Content Provider	IEEE Xplore Digital Library
Author	Hongqing Cao Recknagel, F. Orr, P.T.
Copyright Year	2012
Description	Author affiliation: Seqwater, PO Box 16146, City East Qld, 4002, Australia (Orr, P.T.) \|\| School of Earth and Environmental Sciences, University of Adelaide, 5005, Australia (Hongqing Cao; Recknagel, F.)
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.
Starting Page	1
Ending Page	8
File Size	1117886
Page Count	8
File Format	PDF
ISBN	9781467315104
e-ISBN	9781467315098
e-ISBN	9781467315081
DOI	10.1109/CEC.2012.6252957
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2012-06-10
Publisher Place	Australia
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Australia Biological system modeling Educational institutions Data models Gold Annealing Helium ecological modelling evolutionary algorithm genetic programming parameter optimization population-based algorithm
Content Type	Text
Resource Type	Article

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