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Analysis of a Schnute postulate-based unified growth mode for model selection in evolutionary computations

机译:基于Schnute假设的统一增长模式用于进化计算中模型选择的分析

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摘要

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, 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.
机译:为了评估结合进化算法-信息理论方法从生态学中的一组候选入侵物种模型中选择最佳模型,和/或通过比较它们的相对竞争性,从一组竞争模型中进化出最简约的模型的可行性在性能方面,最好使用涵盖多种情况的统一模型。我们以Schnute的假设为起点,提出了一个统一的增长模型,该模型可以成功地用于进化计算中的模型选择。根据参数设置,统一方程式可以描述几种增长机制。这种包含一系列竞争模型的通用模型机制可以在进化计算算法中成功实现,以进化出最适合地面实况数据的最简约模型。我们已经通过在进化计算模型选择算法中测试反应扩散对流(RDA)模型的有效性来做到这一点。该算法已针对美国尚普兰湖斑马贻贝入侵的现场数据集进行了验证(成功)。

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