Squeeze-and-breathe evolutionary Monte Carlo optimization with local search acceleration and its application to parameter fitting

M. Beguerisse-Díaz, B. Wang, M. Barahona, R. Desikan

Research output: Contribution to journalArticlepeer-review

Abstract / Description of output

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.
Original languageEnglish
Pages (from-to)1925-1933
Number of pages9
JournalJournal of the Royal Society. Interface
Volume9
Issue number73
DOIs
Publication statusPublished - 7 Aug 2012

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