Global optimization has proven to be a powerful tool for solving parameter estimation problems in biological applications, such as the estimation of kinetic rate constants in pathway models. These optimization algorithms sometimes suffer from slow convergence, stagnation or misconvergence to a non-optimal local minimum. Here we show that a nonuniform sampling method (implemented by running the optimization in a transformed space) can improve convergence and robustness for evolutionary-type algorithms, specifically Differential Evolution and Evolutionary Strategies. Results are shown from two case studies exemplifying the common problems of stagnation and misconvergence.
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