title: Analysis of a Schnute postulate-based unified growth model for model selection in evolutionary computations.
abstract: In order to evaluate the feasibility of a combined evolutionary algorithm-information theoretic approach to select the best model from a set of candidate invasive species models in ecology, and/or to evolve the most parsimonious model from a suite of competing models by comparing their relative performance, it is prudent to use a unified model that covers a myriad of situations. Using Schnute's postulates as a starting point [Schnute, J., 1981. A versatile growth model with statistically stable parameters, Can. J. Fish Aquat. Sci. 38, 1128-1140], we present a single, unified model for growth that can be successfully utilized for model selection in evolutionary computations. Depending on the parameter settings, the unified equation can describe several growth mechanisms. Such a generalized model mechanism, which encompasses a suite of competing models, can be successfully implemented in evolutionary computational algorithms to evolve the most parsimonious model that best fits ground truth data. We have done exactly this by testing the effectiveness of our reaction-diffusion-advection (RDA) model in an evolutionary computation model selection algorithm. The algorithm was validated (with success) against field data sets of the Zebra mussel invasion of Lake Champlain in the United States.
authors.affiliation: Department of Mathematics and Statistics, The University of Vermont, Burlington, VT 05405, USA. dbentil@uvm.edu
authors.forename: D E
authors.initials: DE
authors.lastname: Bentil
authors.forename: B M
authors.initials: BM
authors.lastname: Osei
authors.forename: C D
authors.initials: CD
authors.lastname: Ellingwood
authors.forename: J P
authors.initials: JP
authors.lastname: Hoffmann
mesh_terms: D000465:Algorithms; D000818:Animals; D005075:Biological Evolution; D049872:Bivalvia; D004058:Diffusion; D019143:Evolution, Molecular; D008954:Models, Biological; D015233:Models, Statistical; D008962:Models, Theoretical; D010363:Pattern Recognition, Automated; D011158:Population Growth; D012984:Software; D049490:Systems Biology