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

Authors: James Nicholas Furze; Quanmin Zhu; Feng Qiao; Jennifer Hill

Addresses: Faculty of Environment and Technology, Department of Engineering, Design and Mathematics, Department of Geography and Environmental Management, University of the West of England, Frenchay Campus, Bristol, BS16 1QY, UK ' Faculty of Environment and Technology, Department of Engineering, Design and Mathematics, University of the West of England, Frenchay Campus, Bristol, BS16 1QY, UK ' Faculty of Information and Control Engineering, Shenyang JianZhu University, 9 Hunnan East Road, Hunnan New District, Shenyang, 110168, China ' Faculty of Environment and Technology, Department of Geography and Environmental Management, University of the West of England, Frenchay Campus, Bristol, BS16 1QY, UK

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.

Keywords: water-energy dynamic; adaptive neural fuzzy inference systems; ANFIS; genetic algorithms; neural networks; fuzzy logic; optimisation; stochastic distribution; plant strategy; fixed population sizes; utopia hyperplane; temperature; precipitation; modelling; plant distribution.

DOI: 10.1504/IJCAT.2013.054303

International Journal of Computer Applications in Technology, 2013 Vol.47 No.1, pp.68 - 77

Published online: 03 Jun 2013 *

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