论文标题
应用进化元启发术进行基于个体模型的参数估计
Applying Evolutionary Metaheuristics for Parameter Estimation of Individual-Based Models
论文作者
论文摘要
基于个体的模型是复杂的，通常具有较高的输入参数，必须调整这些参数以重现观察到的人群数据或尽可能准确的实验结果。因此，这种建模方法的最弱点之一在于，建模者很少有足够的信息有关输入参数的正确值甚至可接受的范围。因此，必须尝试尝试几种参数组合，以找到一组可接受的输入因素，以最大程度地减少模拟数据集的偏差和参考数据集的偏差。实际上，大多数情况下，遍历完整的搜索空间在计算上是不可行的，尝试所有可能的组合以找到一组最佳的参数。这正是组合问题的一个实例，该实例适合通过元启发式和进化计算技术解决。在这项工作中，我们介绍了Evoper，这是一个用于使用进化计算方法简化参数估计的R软件包。
Individual-based models are complex and they have usually an elevated number of input parameters which must be tuned for reproducing the observed population data or the experimental results as accurately as possible. Thus, one of the weakest points of this modelling approach lies on the fact that rarely the modeler has the enough information about the correct values or even the acceptable range for the input parameters. Consequently, several parameter combinations must be tried to find an acceptable set of input factors minimizing the deviations of simulated and the reference dataset. In practice, most of times, it is computationally unfeasible to traverse the complete search space trying all every possible combination to find the best of set of parameters. That is precisely an instance of a combinatorial problem which is suitable for being solved by metaheuristics and evolutionary computation techniques. In this work, we introduce EvoPER, an R package for simplifying the parameter estimation using evolutionary computation methods.
