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Implementing stochastic distribution within the utopia plane of primary producers using a hybrid genetic algorithm

Furze, James Nicholas; Qiao, Feng; Furze, James; Qaio, Feng; Zhu, Quanmin; Hill, Jennifer

Authors

James Nicholas Furze

Feng Qiao

James Furze

Feng Qaio

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Quan Zhu Quan.Zhu@uwe.ac.uk
Professor in Control Systems

Jenny Hill Jennifer.Hill@uwe.ac.uk
Associate Professor in Teaching and Learning



Abstract

The two key variables in estimating the water-energy dynamic, which determines proportions of plant strategy components on a macro basis, are temperature and precipitation. Additionally, use of high-resolution elevation data facilitates formation of the fuzzy rule base for ordination of the strategical nodes. Application of adaptive neural fuzzy inference systems produces sets of rules, which may be minimised to increase the efficiency of modelling the distribution of plants and their characters. A modified objective genetic evolutionary algorithm was employed in this study to show the distribution of elements of strategies within a strength Pareto. Distribution of the elements showed an approximate Poisson curve in objective space that may be extrapolated to a real-numbered population via application of optimisation algorithms to reflect the stochastic organisation of the populations. © 2013 Inderscience Enterprises Ltd.

Citation

Qiao, F., Furze, J. N., Furze, J., Zhu, Q., Qaio, F., & Hill, J. (2013). Implementing stochastic distribution within the utopia plane of primary producers using a hybrid genetic algorithm. International Journal of Computer Applications in Technology, 47(1), 68-77. https://doi.org/10.1504/IJCAT.2013.054303

Journal Article Type Article
Acceptance Date Jan 1, 2013
Publication Date Jun 12, 2013
Journal International Journal of Computer Applications in Technology
Print ISSN 0952-8091
Publisher Inderscience
Peer Reviewed Peer Reviewed
Volume 47
Issue 1
Pages 68-77
DOI https://doi.org/10.1504/IJCAT.2013.054303
Keywords water-energy dynamic; adaptive neural fuzzy inference systems; genetic algorithm; optimisation; stochastic organisation
Public URL https://uwe-repository.worktribe.com/output/938070
Publisher URL http://dx.doi.org/10.1504/IJCAT.2013.054303