Squeeze-and-Breathe进化蒙特卡洛优化与局部搜索加速及其在参数拟合中的应用
Squeeze-and-Breathe Evolutionary Monte Carlo Optimisation with Local Search Acceleration and its application to parameter fitting
AI总结：
提出一种结合进化算法、序贯蒙特卡洛和直接搜索优化的参数拟合算法，通过局部优化与历史先验重采样迭代改进参数分布，无需参数先验知识即可高效估计。
摘要：
动机：从数据中估计参数是建模过程的关键阶段，尤其是在生物系统中，许多参数需要从稀疏且嘈杂的数据集中估计。多年来，人们提出了各种启发式方法来解决这一复杂的优化问题，在某些情况下取得了良好结果，但在生物背景下存在局限性。结果：在这项工作中，我们开发了一种模型参数拟合算法，该算法结合了进化算法、序贯蒙特卡洛和直接搜索优化的思想。即使参数的量级和/或范围未知，我们的方法也表现良好。该方法通过局部优化与从定义在所有先前迭代支持上的历史先验中进行部分重采样，迭代地细化一系列参数分布。我们使用模拟和真实实验数据以生物模型为例展示了我们的方法，即使在缺乏参数先验知识的情况下也能有效估计参数。
英文摘要：
Motivation: Estimating parameters from data is a key stage of the modelling process, particularly in biological systems where many parameters need to be estimated from sparse and noisy data sets. Over the years, a variety of heuristics have been proposed to solve this complex optimisation problem, with good results in some cases yet with limitations in the biological setting. Results: In this work, we develop an algorithm for model parameter fitting that combines ideas from evolutionary algorithms, sequential Monte Carlo and direct search optimisation. Our method performs well even when the order of magnitude and/or the range of the parameters is unknown. The method refines iteratively a sequence of parameter distributions through local optimisation combined with partial resampling from a historical prior defined over the support of all previous iterations. We exemplify our method with biological models using both simulated and real experimental data and estimate the parameters efficiently even in the absence of a priori knowledge about the parameters.
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