Abstract
Ensemble modeling is a well established approach for obtaining robust predictions and for simulating course grained population behavior in deterministic mathematical models. In this study, we present a multiobjective based technique to estimate model ensembles, the Pareto Optimal Ensemble Technique in the Julia programming language (JuPOETs). JuPOETs integrates simulated annealing with Pareto optimality to estimate parameter ensembles on or near the optimal tradeoff surface between competing training objectives. We demonstrated JuPOETs on a suite of multiobjective problems, including test functions with parameter bounds and system constraints as well as for the identification of a proof-of-concept biochemical model with four conflicting training objectives. JuPOETs identified optimal or near optimal solutions approximately six-fold faster than a corresponding implementation in Octave for the suite of test functions. For the proof-of-concept biochemical model, JuPOETs produced an ensemble of parameters that gave both the mean of the training data for conflicting data sets, while simultaneously estimating parameter sets that performed well on each of the individual objective functions. JuPOETs can be adapted to solve many problem types, including mixed binary and continuous variable types, bilevel optimization problems and constrained problems without altering the base algorithm. JuPOETs can be installed using the Julia package manager from the JuPOETs GitHub repository at https://github.com/varnerlab/POETs.jl.
