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Squeeze-and-Breathe Evolutionary Monte Carlo Optimisation with Local Search Acceleration and its application to parameter fitting

Mariano Beguerisse-Díaz (m.beguerisse-diaz08***at***imperial.ac.uk)
Baojun Wang (b.wang06***at***imperial.ac.uk)
Radhika Desikan (r.desikan***at***imperial.ac.uk)
Mauricio Barahona (m.barahona***at***imperial.ac.uk)

Abstract: 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. 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.

Keywords: Parameter fitting, evolutionary algorithms, sequential Monte Carlo, direct-search optimisation, biological modelling

Category 1: Nonlinear Optimization (Systems governed by Differential Equations Optimization )

Category 2: Applications -- Science and Engineering

Category 3: Global Optimization (Applications )

Citation: Mariano Beguerisse-Díaz, Baojun Wang, Radhika Desikan, and Mauricio Barahona Squeeze-and-breathe evolutionary Monte Carlo optimization with local search acceleration and its application to parameter fitting J. R. Soc. Interface 2012, doi:10.1098/rsif.2011.0767

Download: [PDF]

Entry Submitted: 01/06/2012
Entry Accepted: 01/06/2012
Entry Last Modified: 01/19/2012

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