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

TL;DR

This paper introduces a novel optimization algorithm combining evolutionary strategies, Monte Carlo methods, and local search to efficiently estimate parameters in biological models from sparse, noisy data without prior parameter knowledge.

Contribution

The authors develop a new parameter fitting algorithm that integrates evolutionary, Monte Carlo, and local search techniques, improving robustness and efficiency in biological data contexts.

Findings

Performs well with unknown parameter scales and ranges.

Efficiently estimates parameters from real and simulated biological data.

Refines parameter distributions iteratively with local optimization.

Abstract

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.