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

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dc.contributor.authorCao, H.
dc.contributor.authorRecknagel, F.
dc.contributor.authorOrr, P.
dc.contributor.conferenceIEEE Congress on Evolutionary Computation (2012 : Brisbane, Qld.)
dc.date.issued2012
dc.description.abstractThis 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.
dc.description.statementofresponsibilityHongqing Cao, Friedrich Recknagel, Philip T. Orr
dc.description.urihttp://www.ieee-wcci2012.org/
dc.identifier.citationProceedings of the 2012 IEEE Congress on Evolutionary Computation, held in Brisbane, 10-15 June, 2012: pp.1-8
dc.identifier.doi10.1109/CEC.2012.6252957
dc.identifier.isbn9781467315104
dc.identifier.orcidRecknagel, F. [0000-0002-1028-9413]
dc.identifier.urihttp://hdl.handle.net/2440/75047
dc.language.isoen
dc.publisherIEEE
dc.publisher.placeUSA
dc.relation.ispartofseriesIEEE Congress on Evolutionary Computation
dc.rightsU.S. Government work not protected by U.S. copyright
dc.source.urihttps://doi.org/10.1109/cec.2012.6252957
dc.subjectecological modelling
dc.subjectevolutionary algorithm
dc.subjectgenetic programming
dc.subjectparameter optimization
dc.subjectpopulation-based algorithm
dc.titleThe experimental study of population-based parameter optimization algorithms on rule-based ecological modelling
dc.typeConference paper
pubs.publication-statusPublished

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