Squeeze-and-breathe evolutionary Monte Carlo optimization with local search acceleration and its application to parameter fitting
Squeeze-and-breathe evolutionary Monte Carlo optimization with local search acceleration and its application to parameter fitting
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DOI：
10.1098/rsif.2011.0767
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发表时间：
2012-08-07
影响因子：
3.9
通讯作者：
Barahona, Mauricio
中科院分区：
文献类型：
--
作者：
Beguerisse-Diaz, Mariano;Wang, Baojun;Barahona, Mauricio
从数据中估计参数是建模过程的一个关键阶段，特别是在生物系统中，许多参数需要从稀疏和嘈杂的数据集中估计。多年来，已经提出了各种方法来解决这个复杂的优化问题，在某些情况下取得了良好的效果，但在生物环境中存在局限性。在这项工作中，我们开发了一个算法模型参数拟合，结合进化算法，顺序蒙特卡罗和直接搜索优化的想法。我们的方法执行良好，即使当数量级和/或参数的范围是未知的。该方法通过局部优化结合部分恢复迭代地细化一系列参数分布，该部分恢复来自在所有先前迭代的支持下定义的历史先验。我们使用模拟和真实的实验数据与生物模型，我们的方法，估计参数有效，即使在没有先验知识的参数。
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 datasets. Over the years, a variety of heuristics have been proposed to solve this complex optimization problem, with good results in some cases yet with limitations in the biological setting. In this work, we develop an algorithm for model parameter fitting that combines ideas from evolutionary algorithms, sequential Monte Carlo and direct search optimization. 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 optimization 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.